text:
= {125601},
  issn = {0096-3003},
  doi = {10.1016/j.amc.2020.125601},
  abstract = {Because of its broad real-life application, community detection (in the realm of a complex network) is an attractive challenge to many researchers. However, current methods fail to reveal the full community structure and its formation process. Thus, here we present SIMGT, an effective and Scalable approach that detects overlapping communities: Integrating social identity Model and Game Theory. Inspired by social identity theory and nodes' high-order proximities, first we weight and rewire the original network, then we associate each node with a new utility function. Next, we model community formation as a non-cooperative game among all nodes, and we regard the nodes as self-interested players. Further, we use a stochastic gradient-ascent method to update players' strategies toward different communities, and prove that our game greatly resembles and matches how a potential game works (in the classical sense in game theory), indicating that the Nash equilibrium point must exist. Finally, we implement comprehensive experiments on several synthetic and real-life networks. The results show that whatever weighting strategy we choose, SIMGT can gain better performance on community detection task. In particular, SIMGT achieves a best result when we choose the Jaccard coefficient. After comparing SIMGT with six benchmark algorithms, we obtain convincing results in terms of how well the algorithms reveal communities, as well as algorithms' scalability.},
  file = {/home/aleksandar/Zotero/storage/3Y99CL6E/S0096300320305567.html},
  journal = {Applied Mathematics and Computation},
  language = {en}
}

@article{wangFastNetworkCommunity2020,
  title = {Fast {{Network Community Detection}} with {{Profile}}-{{Pseudo Likelihood Methods}}},
  author = {Wang, Jiangzhou and Zhang, Jingfei and Liu, Binghui and Zhu, Ji and Guo, Jianhua},
  year = {2020},
  month = nov,
  abstract = {The stochastic block model is one of the most studied network models for community detection. It is well-known that most algorithms proposed for fitting the stochastic block model likelihood function cannot scale to large-scale networks. One prominent work that overcomes this computational challenge is Amini et al.(2013), which proposed a fast pseudo-likelihood approach for fitting stochastic block models to large sparse networks. However, this approach does not have convergence guarantee, and is not well suited for small- or medium- scale networks. In this article, we propose a novel likelihood based approach that decouples row and column labels in the likelihood function, which enables a fast alternating maximization; the new method is computationally efficient, performs well for both small and large scale networks, and has provable convergence guarantee. We show that our method provides strongly consistent estimates of the communities in a stochastic block model. As demonstrated in simulation studies, the proposed method outperforms the pseudo-likelihood approach in terms of both estimation accuracy and computation efficiency, especially for large sparse networks. We further consider extensions of our proposed method to handle networks with degree heterogeneity and bipartite properties.},
  archivePrefix = {arXiv},
  eprint = {2011.00647},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Wang et al/Wang et al_2020_Fast Network Community Detection with Profile-Pseudo Likelihood Methods.pdf;/home/aleksandar/Zotero/storage/LKKFPIUC/2011.html},
  journal = {arXiv:2011.00647 [math, stat]},
  keywords = {Mathematics - Statistics Theory,Statistics - Machine Learning,Statistics - Methodology},
  primaryClass = {math, stat}
}

@article{wangFreeUtilityModel2020,
  title = {Free Utility Model for Explaining the Social Gravity Law},
  author = {Wang, Hao and Yan, Xiao-Yong and Wu, Jinshan},
  year = {2020},
  month = sep,
  abstract = {Social gravity law widely exists in human travel, population migration, commodity trade, information communication, scientific collaboration and so on. Why is there such a simple law in many complex social systems is an interesting question. Although scientists from fields of statistical physics, complex systems, economics and transportation science have explained the social gravity law, a theoretical explanation including two dominant mechanisms, namely individual interaction and bounded rationality, is still lacking. Here we present a free utility model from the perspective of individual choice behavior to explain the social gravity law. The basic assumption is that bounded rational individuals interacting with each other will trade off the expected utility and information-processing cost to maximize their own utility. The previous explanations of the social gravity law including the maximum entropy model, the free cost model, the Logit model and the destination choice game model are all special cases under our model. Further, we extend the free utility model to the dummy network and real transportation network. This model not only helps us to better understand the underlying mechanisms of spatial interaction patterns in complex social systems, but also provides a new perspective for understanding the potential function in game theory and the user equilibrium model in transportation science.},
  archivePrefix = {arXiv},
  eprint = {2009.07984},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Wang et al/Wang et al_2020_Free utility model for explaining the social gravity law.pdf;/home/aleksandar/Zotero/storage/SAKVZN75/2009.html},
  journal = {arXiv:2009.07984 [physics]},
  primaryClass = {physics}
}

@misc{wangInfluenceNetworkStructural2020,
  title = {The {{Influence}} of {{Network Structural Preference}} on {{Link Prediction}}},
  author = {Wang, Yongcheng and Wang, Yu and Lin, Xinye and Wang, Wei},
  year = {2020},
  month = sep,
  volume = {2020},
  pages = {e6148273},
  publisher = {{Hindawi}},
  issn = {1026-0226},
  doi = {10.1155/2020/6148273},
  abstract = {Link prediction in complex networks predicts the possibility of link generation between two nodes that have not been linked yet in the network, based on known network structure and attributes. It can be applied in various fields, such as friend recommendation in social networks and prediction of protein-protein interaction in biology. However, in the social network, link prediction may raise concerns about privacy and security, because, through link prediction algorithms, criminals can predict the friends of an account user and may even further discover private information such as the address and bank accounts. Therefore, it is urgent to develop a strategy to prevent being identified by link prediction algorithms and protect privacy, utilizing perturbation on network structure at a low cost, including changing and adding edges. This article mainly focuses on the influence of network structural preference perturbation through deletion on link prediction. According to a large number of experiments on the various real networks, edges between large-small degree nodes and medium-medium degree nodes have the most significant impact on the quality of link prediction.},
  file = {/home/aleksandar/Documents/Zotero/Wang et al/Wang et al_2020_The Influence of Network Structural Preference on Link Prediction.pdf},
  howpublished = {https://www.hindawi.com/journals/ddns/2020/6148273/},
  journal = {Discrete Dynamics in Nature and Society},
  language = {en},
  type = {Research {{Article}}}
}

@article{wangJointAnalysisSocial2019,
  title = {Joint {{Analysis}} of {{Social}} and {{Item Response Networks}} with {{Latent Space Models}}},
  author = {Wang, Selena Shuo and Paul, Subhadeep and Logan, Jessica and De Boeck, Paul},
  year = {2019},
  month = oct,
  abstract = {The adjustment of students to a school environment is fundamentally linked to the friendship networks they form with their peers. Consequently, the complete picture of a student's adjustment can only be obtained by taking into account both their friendship network and their reported perceptions of the school environment. However, there is a lack of flexible statistical models and methods that can jointly analyze a social network with an item-response data matrix. In this paper, we propose a latent space model for heterogeneous (multimodal) networks (LSMH) and its extension LSMH-Item, which combine the framework of latent space modeling in network analysis with item response theory in psychometrics. Using LSMH, we summarize the information from the social network and the item responses in a person-item joint latent space. We use a Variational Bayesian Expectation-Maximization estimation algorithm to estimate the item and person locations in the joint latent space. This methodology allows for effective integration, informative visualization, and prediction of social networks and item responses. We apply the proposed methodology to data collected from 16 third-grade classrooms comprised of 451 third-grade students' self-reported friendships and school liking, which were collected as part of the Early Learning Ohio project. Through the person-item joint latent space, we are able to identify students with potential adjustment difficulties and found consistent connection between students' friendship networks and their well-being. We believe that using LSMH, researchers will be able to easily identify students in need of intervention and revolutionize the understanding of social behaviors.},
  archivePrefix = {arXiv},
  eprint = {1910.12128},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Dropbox/Zotero/Wang et al/Wang et al_2019_Joint Analysis of Social and Item Response Networks with Latent Space Models4.pdf;/home/aleksandar/Zotero/storage/2XHFY2HG/wangJointAnalysisSocial2019.html},
  journal = {arXiv:1910.12128 [stat]},
  primaryClass = {stat}
}

@article{wangMathematicalTheoryCooperative2019,
  title = {A Mathematical Theory of Cooperative Communication},
  author = {Wang, Pei and Wang, Junqi and Paranamana, Pushpi and Shafto, Patrick},
  year = {2019},
  month = oct,
  abstract = {Cooperative communication plays a central role in theories of human cognition, language, development, and culture, and is increasingly relevant in human-algorithm and robot interaction. Existing models are algorithmic in nature and do not shed light on the statistical problem solved in cooperation or on constraints imposed by violations of common ground. We present a mathematical theory of cooperative communication that unifies three broad classes of algorithmic models as approximations of Optimal Transport (OT). We derive a statistical interpretation for the problem approximated by existing models in terms of entropy minimization, or likelihood maximizing, plans. We show that some models are provably robust to violations of common ground, even supporting online, approximate recovery from discovered violations, and derive conditions under which other models are provably not robust. We do so using gradient-based methods which introduce novel algorithmic-level perspectives on cooperative communication. Our mathematical approach complements and extends empirical research, providing strong theoretical tools derivation of a priori constraints on models and implications for cooperative communication in theory and practice.},
  archivePrefix = {arXiv},
  eprint = {1910.02822},
  eprinttype = {arxiv},
  journal = {arXiv:1910.02822 [cs, stat]},
  language = {en},
  primaryClass = {cs, stat}
}

@article{wangModelAdequacyChecking2020,
  title = {Model {{Adequacy Checking}}/{{Goodness}}-of-Fit {{Testing}} for {{Behavior}} in {{Joint Dynamic Network}}/{{Behavior Models}}, with an {{Extension}} to {{Two}}-Mode {{Networks}}},
  author = {Wang, Cheng and Butts, Carter T. and Hipp, John and Lakon, Cynthia M.},
  year = {2020},
  month = apr,
  pages = {0049124120914933},
  publisher = {{SAGE Publications Inc}},
  issn = {0049-1241},
  doi = {10.1177/0049124120914933},
  abstract = {The recent popularity of models that capture the dynamic coevolution of both network structure and behavior has driven the need for summary indices to assess the adequacy of these models to reproduce dynamic properties of scientific or practical importance. Whereas there are several existing indices for assessing the ability of the model to reproduce network structure over time, to date there are few indices for assessing the ability of the model to reproduce individuals' behavior patterns. Drawing on the widely used strategy of assessing model adequacy by comparing index values summarizing features of the observed data to the distribution of those index values on simulated data from the fitted model, we propose four goals that a researcher could reasonably expect of a joint structure/behavior model regarding how well it captures behavior and describe indices for assessing each of these. These reasonably simple and easily implemented indices can be used for assessing model adequacy with any dynamic network models jointly working with networks and behavior, including the stochastic actor-based models implemented within software packages such as RSien version 1.2-24. We demonstrate the use of our indices with an empirical example to show how they can be employed in practical settings, with an additional extension to modeling affiliation dynamics in two-mode networks. Key scripts are provided in the Supplemental Document (which can be found at http://smr.sagepub.com/supplemental/).},
  journal = {Sociological Methods \& Research},
  language = {en}
}

@article{wangNeitherInfluenceSelection2020,
  title = {Neither Influence nor Selection: {{Examining}} Co-Evolution of Political Orientation and Social Networks in the {{NetSense}} and {{NetHealth}} Studies},
  shorttitle = {Neither Influence nor Selection},
  author = {Wang, Cheng and Lizardo, Omar and Hachen, David S.},
  year = {2020},
  month = may,
  volume = {15},
  pages = {e0233458},
  publisher = {{Public Library of Science}},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0233458},
  abstract = {Political orientation is one of the most important and consequential individual attributes studied by social scientists. Yet, we know relatively little about the temporal evolution of political orientation, especially at periods in the life course during which individuals are forming new social relationships and transitioning to new relational contexts. Here we use Stochastic Actor-Oriented models (SAOMs) to examine the co-evolution of political orientation and social networks using two feature-rich, temporal network datasets from samples of students making the transition to college at the University of Notre Dame (i.e. the NetSense and NetHealth studies). Overall, we find a great deal of stability in political orientation, with a slight tendency for the 2011 NetSense study participants to become more conservative during their first four semesters in college, but not the 2015 NetHealth study participants. Partisanship is the best predictor of changes in political orientation, with students who identify or vote as Republicans becoming more conservative over time. Neither network influence nor selection processes seem to be driving observed changes. During this formative period, relatively stable identities such as party affiliation predict changes in political orientation independently of local network dynamics, selection processes, socio-demographic traits, and dispositional factors.},
  journal = {PLOS ONE},
  language = {en},
  number = {5}
}

@article{wangPromotingCooperationReputation2021,
  title = {Promoting Cooperation by Reputation Scoring Mechanism Based on Historical Donations in Public Goods Game},
  author = {Wang, Le and Chen, Tong and Wu, Zhenghong},
  year = {2021},
  month = feb,
  volume = {390},
  pages = {125605},
  issn = {0096-3003},
  doi = {10.1016/j.amc.2020.125605},
  abstract = {In an acquaintance society, reputation plays an important role in individuals' activities. Especially when we face with collective decision-making, such as crowd-funding. The existence of reputation could stimulate cooperation. In this work, individuals' reputation has been divided into being good, bad and medium according to their neighbors' judgement. Considering people with different requirements of reputation, we put forward a new reputation scoring mechanism with tolerance based on historical donations. First, we study the effect of different scoring rules on players' strategies. Based on this, we discuss the effect of neighbors' reputation on players' simulation process. In addition, we explore the population structure's influence on cooperation. Through numerical simulations, we are glad to find that the strict scoring rules could better stimulate cooperation in most circumstances. Moreover, imitation which depends on both reputation and payoff could promote cooperation. And individuals with good reputation have better effect on cooperation compared to those with bad reputation. This work provides a new reference for organizers to promote more people to participate and cooperate in Public Goods Game (PGG).},
  file = {/home/aleksandar/Zotero/storage/P2AQAUBW/S0096300320305609.html},
  journal = {Applied Mathematics and Computation},
  language = {en}
}

@article{wangSocialCommunicationModel2020,
  title = {A Social Communication Model Based on Simplicial Complexes},
  author = {Wang, Dong and Zhao, Yi and Leng, Hui and Small, Michael},
  year = {2020},
  month = dec,
  volume = {384},
  pages = {126895},
  issn = {0375-9601},
  doi = {10.1016/j.physleta.2020.126895},
  abstract = {Networks offer a powerful language with which to describe and study pairwise interaction. However, in many contexts, these rich collective phenomena require a higher-order approach to encode dynamical processes \textemdash{} for example in idea integration and information transmission (co-publication is a particularly familiar example). Here we introduce a novel framework for social communication by reshaping the networked system to be a simplicial complex, where the communication involves the interaction not only of individual nodes but also among cliques to which they belong. Simplicial complexes extend the network-based pairwise relationship to multiagent interaction. Assuming that the same individual in different cliques may play different roles, a threshold is designed and combined with the node state to determine the clique state. We employ the discrete microscopic Markov chain approach to model the simplex-based social communication and then obtain the underlying critical condition for information outbreaks. Moreover, we perform extensive numerical analysis of the proposed simplicial complex-based communication model and compare its performance with Monte Carlo simulation.},
  file = {/home/aleksandar/Zotero/storage/APJF5GWP/S0375960120307623.html},
  journal = {Physics Letters A},
  language = {en},
  number = {35}
}

@article{wangTemporalStableCommunity,
  ids = {wangTemporalStableCommunitya},
  title = {Temporal {{Stable Community}} in {{Time}}-{{Varying Networks}}.},
  author = {Wang, Wenjing and Li, Xiang},
  doi = {arXiv:1811.04549v1},
  abstract = {{$<$}p{$>$}Identifying community structure of a complex network provides insight to the interdependence between the network topology and emergent collective behaviors of networks, while detecting such invariant communities in a time-varying network is more challenging. In this paper, we define the temporal stable community and newly propose the concept of dynamic modularity to evaluate the stable community structures in time-varying networks, which is robust against small changes as verified by several empirical time-varying network datasets. Besides, using the volatility features of temporal stable communities in functional brain networks, we successfully differentiate the ADHD (Attention Deficit Hyperactivity Disorder) patients and healthy controls efficiently. {$<$}/p{$>$}},
  journal = {arXiv Physics and Society}
}

@article{wangUnderstandingSocialForceModel,
  title = {Understanding {{Social}}-{{Force Model}} in {{Psychological Principles}} of {{Collective Behavior}}.},
  author = {Wang, Peng},
  doi = {arXiv:1605.05146v10},
  abstract = {{$<$}p{$>$}To well understand crowd behavior, microscopic models have been developed in recent decades, in which an individual's behavioral/psychological status can be modeled and simulated. A well-known model is the social-force model innovated by physical scientists (Helbing and Molnar, 1995; Helbing, Farkas and Vicsek, 2000; Helbing et al., 2002). This model has been widely accepted and mainly used in simulation of crowd evacuation in the past decade. A problem, however, is that the testing results of the model were not explained in consistency with the psychological findings, resulting in misunderstanding of the model by psychologists. This paper will bridge the gap between psychological studies and physical explanation about this model. We reinterpret this physics-based model from a psychological perspective, clarifying that the model is consistent with psychological theories on stress, including time-related stress and interpersonal stress. Based on the conception of stress, we renew the model at both micro-and-macro level, referring to multi-agent simulation in a microscopic sense and fluid-based analysis in a macroscopic sense. The cognition and behavior of individual agents are critically modeled as response to environmental stimuli. Existing simulation results such as faster-is-slower effect will be reinterpreted by Yerkes-Dodson law, and herding and grouping effect are further discussed by integrating attraction into the social force. In brief the social-force model exhibits a bridge between the physics laws and psychological principles regarding crowd motion, and this paper will renew and reinterpret the model on the foundation of psychological studies. {$<$}/p{$>$}},
  journal = {arXiv Physics and Society}
}

@article{wangUnderstandingSocialForceModela,
  title = {Understanding {{Social}}-{{Force Model}} in {{Psychological Principles}} of {{Collective Behavior}}.},
  author = {Wang, Peng},
  doi = {arXiv:1605.05146v10},
  abstract = {{$<$}p{$>$}To well understand crowd behavior, microscopic models have been developed in recent decades, in which an individual's behavioral/psychological status can be modeled and simulated. A well-known model is the social-force model innovated by physical scientists (Helbing and Molnar, 1995; Helbing, Farkas and Vicsek, 2000; Helbing et al., 2002). This model has been widely accepted and mainly used in simulation of crowd evacuation in the past decade. A problem, however, is that the testing results of the model were not explained in consistency with the psychological findings, resulting in misunderstanding of the model by psychologists. This paper will bridge the gap between psychological studies and physical explanation about this model. We reinterpret this physics-based model from a psychological perspective, clarifying that the model is consistent with psychological theories on stress, including time-related stress and interpersonal stress. Based on the conception of stress, we renew the model at both micro-and-macro level, referring to multi-agent simulation in a microscopic sense and fluid-based analysis in a macroscopic sense. The cognition and behavior of individual agents are critically modeled as response to environmental stimuli. Existing simulation results such as faster-is-slower effect will be reinterpreted by Yerkes-Dodson law, and herding and grouping effect are further discussed by integrating attraction into the social force. In brief the social-force model exhibits a bridge between the physics laws and psychological principles regarding crowd motion, and this paper will renew and reinterpret the model on the foundation of psychological studies. {$<$}/p{$>$}},
  journal = {arXiv Physics and Society}
}

@article{wassermanAnalyzingSocialNetworks1980,
  title = {Analyzing {{Social Networks}} as {{Stochastic Processes}}},
  author = {Wasserman, Stanley},
  year = {1980},
  month = jun,
  volume = {75},
  pages = {280--294},
  issn = {0162-1459, 1537-274X},
  doi = {10.1080/01621459.1980.10477465},
  journal = {Journal of the American Statistical Association},
  language = {en},
  number = {370}
}

@book{wassermanSocialNetworkAnalysis1994,
  title = {Social Network Analysis: {{Methods}} and Applications},
  shorttitle = {Social Network Analysis},
  author = {Wasserman, Stanley and Faust, Katherine},
  year = {1994},
  publisher = {{Cambridge University Press}},
  address = {{Cambridge ; New York}},
  isbn = {978-0-521-38269-4 978-0-521-38707-1},
  lccn = {HM131 .W356 1994},
  number = {8},
  series = {Structural Analysis in the Social Sciences}
}

@book{watanabeMathematicalTheoryBayesian2018,
  title = {Mathematical Theory of {{Bayesian}} Statistics},
  author = {Watanabe, Sumio},
  year = {2018},
  publisher = {{CRC Press, Taylor \& Francis Group}},
  address = {{Boca Raton}},
  isbn = {978-1-4822-3806-8},
  lccn = {QA279.5 .W378 2018}
}

@article{wattsCollectiveDynamicsSmallworld1998,
  title = {Collective Dynamics of `Small-World'Networks},
  author = {Watts, Duncan J and Strogatz, Steven H},
  year = {1998},
  volume = {393},
  pages = {440},
  journal = {nature},
  number = {6684}
}

@article{wattsNewScienceNetworks2004,
  title = {The ``{{New}}'' {{Science}} of {{Networks}}},
  author = {Watts, Duncan J.},
  year = {2004},
  month = aug,
  volume = {30},
  pages = {243--270},
  issn = {0360-0572, 1545-2115},
  doi = {10.1146/annurev.soc.30.020404.104342},
  journal = {Annual Review of Sociology},
  language = {en},
  number = {1}
}

@article{webberChapterRecurrenceQuantification,
  title = {Chapter 2. {{Recurrence Quantification Analysis}} of {{Nonlinear Dynamical Systems}}},
  author = {Webber, Charles L},
  pages = {69},
  file = {/home/aleksandar/Zotero/storage/22Q2PSIP/Webber - Chapter 2. Recurrence Quantification Analysis of N.pdf},
  language = {en}
}

@article{weberMethodEvaluateReliability2020,
  title = {A Method to Evaluate the Reliability of Social Media Data for Social Network Analysis},
  author = {Weber, Derek and Nasim, Mehwish and Mitchell, Lewis and Falzon, Lucia},
  year = {2020},
  month = oct,
  abstract = {To study the effects of Online Social Network (OSN) activity on real-world offline events, researchers need access to OSN data, the reliability of which has particular implications for social network analysis. This relates not only to the completeness of any collected dataset, but also to constructing meaningful social and information networks from them. In this multidisciplinary study, we consider the question of constructing traditional social networks from OSN data and then present a measurement case study showing how the reliability of OSN data affects social network analyses. To this end we developed a systematic comparison methodology, which we applied to two parallel datasets we collected from Twitter. We found considerable differences in datasets collected with different tools and that these variations significantly alter the results of subsequent analyses. Our results lead to a set of guidelines for researchers planning to collect online data streams to infer social networks.},
  archivePrefix = {arXiv},
  eprint = {2010.08717},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Weber et al/Weber et al_2020_A method to evaluate the reliability of social media data for social network.pdf;/home/aleksandar/Zotero/storage/2J7HMTSI/2010.html},
  journal = {arXiv:2010.08717 [cs]},
  primaryClass = {cs}
}

@article{weghorstFormlessnessStructureInstitutionalization2014,
  title = {From Formlessness to Structure? {{The}} Institutionalization of Competitive Party Systems in {{Africa}}},
  shorttitle = {From {{Formlessness}} to {{Structure}}?},
  author = {Weghorst, Keith R. and Bernhard, Michael},
  year = {2014},
  month = oct,
  volume = {47},
  pages = {1707--1737},
  issn = {0010-4140, 1552-3829},
  doi = {10.1177/0010414013516068},
  journal = {Comparative Political Studies},
  language = {en},
  number = {12}
}

@article{wegnerAtomicSubgraphsStatistical2020,
  title = {Atomic Subgraphs and the Statistical Mechanics of Networks},
  author = {Wegner, Anatol E. and Olhede, Sofia},
  year = {2020},
  month = aug,
  abstract = {We develop random graph models where graphs are generated by connecting not only pairs of vertices by edges but also larger subsets of vertices by copies of small atomic subgraphs of arbitrary topology. This allows the for the generation of graphs with extensive numbers of triangles and other network motifs commonly observed in many real world networks. More specifically we focus on maximum entropy ensembles under constraints placed on the counts and distributions of atomic subgraphs and derive general expressions for the entropy of such models. We also present a procedure for combining distributions of multiple atomic subgraphs that enables the construction of models with fewer parameters. Expanding the model to include atoms with edge and vertex labels we obtain a general class of models that can be parametrized in terms of basic building blocks and their distributions that includes many widely used models as special cases. These models include random graphs with arbitrary distributions of subgraphs, random hypergraphs, bipartite models, stochastic block models, models of multilayer networks and their degree corrected and directed versions. We show that the entropy for all these models can be derived from a single expression that is characterized by the symmetry groups of atomic subgraphs.},
  archivePrefix = {arXiv},
  eprint = {2008.10346},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Zotero/storage/6H35PV3A/Wegner_Olhede_2020_Atomic subgraphs and the statistical mechanics of networks.pdf;/home/aleksandar/Zotero/storage/RJ7A4I4B/2008.html},
  journal = {arXiv:2008.10346 [physics, stat]},
  primaryClass = {physics, stat}
}

@article{wehnerValidationShortDark2020,
  title = {Validation of the {{Short Dark Triad}} in a {{German Sample}}: {{Structure}}, {{Nomological Network}}, and an {{Ultrashort Version}}},
  shorttitle = {Validation of the {{Short Dark Triad}} in a {{German Sample}}},
  author = {Wehner, Caroline and Maa{\ss}, Ulrike and Leckelt, Marius and Back, Mitja D. and Ziegler, Matthias},
  year = {2020},
  month = nov,
  pages = {1--12},
  issn = {1015-5759, 2151-2426},
  doi = {10.1027/1015-5759/a000617},
  abstract = {The structure, correlates, and assessment of the Dark Triad are widely discussed in several fields of psychology. Based on the German version of the Short Dark Triad (SDT), we add to this by (a) providing a competitive test of existing structural models, (b) testing the nomological network, and (c) proposing an ultrashort 9-item version of the SDT (uSDT). A sample of N = 969 participants provided data on the SDT and a range of further measures. Our competitive test of five structural models revealed that fit indices and nomological network assumptions were best met in a three-factor model, with separate factors for psychopathy, Machiavellianism, and narcissism. The results provided an extensive overview of the raw, unique, and shared associations of Dark Triad dimensions with narcissism facets, sadism, impulsivity, self-esteem, sensation seeking, the Big Five, maladaptive personality traits, sociosexual orientation, and behavioral criteria. Finally, the uSDT exhibited satisfactory psychometric properties. The highest overlap in expected relations between SDT and uSDT, and convergent and discriminant measures was also found for the three-factor model. Our study underlines the utility of a three-factor model of the Dark Triad, extends findings on its nomological network, and provides an ultrashort instrument.},
  file = {/home/aleksandar/Zotero/storage/W4XVDNBC/Validation-of-the-Short-Dark-Triad-in-a-German-Sample.pdf},
  journal = {European Journal of Psychological Assessment},
  keywords = {TODO},
  language = {en}
}

@article{weiCommunityDetectionRiemannian2020,
  title = {Community {{Detection}} by a {{Riemannian Projected Proximal Gradient Method}}},
  author = {Wei, Meng and Huang, Wen and Gallivan, Kyle A. and Van Dooren, Paul},
  year = {2020},
  month = sep,
  abstract = {Community detection plays an important role in understanding and exploiting the structure of complex systems. Many algorithms have been developed for community detection using modularity maximization or other techniques. In this paper, we formulate the community detection problem as a constrained nonsmooth optimization problem on the compact Stiefel manifold. A Riemannian projected proximal gradient method is proposed and used to solve the problem. To the best of our knowledge, this is the first attempt to use Riemannian optimization for community detection problem. Numerical experimental results on synthetic benchmarks and real-world networks show that our algorithm is effective and outperforms several state-of-art algorithms.},
  archivePrefix = {arXiv},
  eprint = {2009.11989},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Wei et al/Wei et al_2020_Community Detection by a Riemannian Projected Proximal Gradient Method.pdf;/home/aleksandar/Zotero/storage/VQKFXW3M/2009.html},
  journal = {arXiv:2009.11989 [cs, math]},
  primaryClass = {cs, math}
}

@article{weirMultilayerModularityBelief2020,
  title = {Multilayer {{Modularity Belief Propagation}} to {{Assess Detectability}} of {{Community Structure}}},
  author = {Weir, William H. and Walker, Benjamin and Zdeborov{\'a}, Lenka and Mucha, Peter J.},
  year = {2020},
  month = jan,
  volume = {2},
  pages = {872--900},
  issn = {2577-0187},
  doi = {10.1137/19M1279812},
  abstract = {Modularity-based community detection encompasses a number of widely used, efficient heuristics for identification of structure in networks. Recently, a belief propagation approach to modularity optimization provided a useful guide for identifying nontrivial structure in single-layer networks in a way that other optimization heuristics have not. In this paper, we extend modularity belief propagation to multilayer networks. As part of this development, we also directly incorporate a resolution parameter. We show that adjusting the resolution parameter affects the convergence properties of the algorithm and yields different community structures than the baseline. We compare our approach with a widely used community detection tool, GenLouvain, across a range of synthetic, multilayer benchmark networks, demonstrating that our method performs comparably to the state of the art. Finally, we demonstrate the practical advantages of the additional information provided by our tool by way of two real-world network examples. We show how the convergence properties of the algorithm can be used in selecting the appropriate resolution and coupling parameters and how the node-level marginals provide an interpretation for the strength of attachment to the identified communities. We have released our tool as a Python package for convenient use.},
  file = {/home/aleksandar/Zotero/storage/TKCQ7CGI/Weir et al. - 2020 - Multilayer Modularity Belief Propagation to Assess.pdf},
  journal = {SIAM Journal on Mathematics of Data Science},
  language = {en},
  number = {3}
}

@article{welzelCulturalTheoryRegimes2019,
  title = {A Cultural Theory of Regimes},
  author = {Welzel, Christian},
  year = {2019},
  month = dec,
  pages = {1--2},
  issn = {2397-3374},
  doi = {10.1038/s41562-019-0790-4},
  abstract = {There is a longstanding debate about whether culture shapes regimes or regimes shape culture. New research by Ruck et al. resolves the debate in favor of culture's causal primacy.},
  copyright = {2019 Springer Nature Limited},
  file = {/home/aleksandar/Zotero/storage/L6YKCNIF/s41562-019-0790-4.html},
  journal = {Nature Human Behaviour},
  language = {en}
}

@misc{wenEvolutionaryGameAnalysis2020,
  title = {An {{Evolutionary Game Analysis}} of {{Internet Public Opinion Events}} at {{Universities}}: {{A Case}} from {{China}}},
  shorttitle = {An {{Evolutionary Game Analysis}} of {{Internet Public Opinion Events}} at {{Universities}}},
  author = {Wen, Hongying and Liang, Kairong and Li, Yiquan},
  year = {2020},
  month = jul,
  volume = {2020},
  pages = {e8596717},
  publisher = {{Hindawi}},
  issn = {1024-123X},
  doi = {10.1155/2020/8596717},
  abstract = {Internet public opinion events at universities in China occurred frequently, creating painful repercussions for reputation and stability of colleges and universities. To better cope with the problem, this paper explores an evolutionary mechanism of the university Internet public opinion events. Firstly, we discuss the interactions and behavior of three key participants: an Internet medium, university students as a whole, and administration. Secondly, we construct a tripartite evolutionary game model consisting of an Internet medium, student group, and university administration and then analyze and obtain the differential dynamic equations and equilibrium points. Subsequently, the evolutionary stable equilibrium is further analyzed. Finally, we employ numerical studies to examine how the tripartite behavior choices affect evolutionary paths and evolutionary equilibrium strategies. Results are derived as follows: under certain conditions, there exists an asymptotically stable equilibrium point for the tripartite evolutionary game. On the one hand, appropriate penalties and rewards should be provided to foster objectives and fair behaviors of the network medium. On the other hand, university students should be educated and guided to deal rationally with negative effects of Internet public opinion events. Moreover, online real-name authentication is an important and necessary measure. Finally, the university administration should release truthful, timely, and comprehensive information of Internet public opinion events to mitigate potential negative impacts.},
  file = {/home/aleksandar/Dropbox/Zotero/Wen et al/Wen et al_2020_An Evolutionary Game Analysis of Internet Public Opinion Events at Universities.pdf;/home/aleksandar/Zotero/storage/3WFAT54N/8596717.html},
  howpublished = {https://www.hindawi.com/journals/mpe/2020/8596717/},
  journal = {Mathematical Problems in Engineering},
  language = {en},
  type = {Research {{Article}}}
}

@book{wertheimer-baleticStanovnistvoRazvoj1999,
  title = {Stanovni\v{s}tvo i Razvoj},
  author = {{Wertheimer-Baleti{\'c}}, Alica},
  year = {1999},
  publisher = {{Mate}},
  address = {{Zagreb}}
}

@article{westRelatingSizeFunctionality2020,
  title = {Relating Size and Functionality in Human Social Networks through Complexity},
  author = {West, B. J. and Massari, G. F. and Culbreth, G. and Failla, R. and Bologna, M. and Dunbar, R. I. M. and Grigolini, P.},
  year = {2020},
  month = jul,
  pages = {202006875},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.2006875117},
  abstract = {Extensive empirical evidence suggests that there is a maximal number of people with whom an individual can maintain stable social relationships (the Dunbar number). We argue that this arises as a consequence of a natural phase transition in the dynamic self-organization among               N               individuals within a social system. We present the calculated size dependence of the scaling properties of complex social network models to argue that this collective behavior is an enhanced form of collective intelligence. Direct calculation establishes that the complexity of social networks as measured by their scaling behavior is nonmonotonic, peaking around 150, thereby providing a theoretical basis for the value of the Dunbar number. Thus, we establish a theory-based bridge spanning the gap between sociology and psychology.},
  file = {/home/aleksandar/Zotero/storage/DJX8ZY28/West et al. - 2020 - Relating size and functionality in human social ne.pdf},
  journal = {Proceedings of the National Academy of Sciences},
  language = {en}
}

@article{wharrieMicroMesoMacroscales2019,
  title = {Micro-, Meso-, Macroscales: {{The}} Effect of Triangles on Communities in Networks},
  shorttitle = {Micro-, Meso-, Macroscales},
  author = {Wharrie, Sophie and Azizi, Lamiae and Altmann, Eduardo G.},
  year = {2019},
  month = aug,
  volume = {100},
  pages = {022315},
  publisher = {{American Physical Society}},
  doi = {10.1103/PhysRevE.100.022315},
  abstract = {Mesoscale structures (communities) are used to understand the macroscale properties of complex networks, such as their functionality and formation mechanisms. Microscale structures are known to exist in most complex networks (e.g., large number of triangles or motifs), but they are absent in the simple random-graph models considered (e.g., as null models) in community-detection algorithms. In this paper we investigate the effect of microstructures on the appearance of communities in networks. We find that alone the presence of triangles leads to the appearance of communities even in methods designed to avoid the detection of communities in random networks. This shows that communities can emerge spontaneously from simple processes of motiff generation happening at a microlevel. Our results are based on four widely used community-detection approaches (stochastic block model, spectral method, modularity maximization, and the Infomap algorithm) and three different generative network models (triadic closure, generalized configuration model, and random graphs with triangles).},
  file = {/home/aleksandar/Documents/Zotero/Wharrie et al/Wharrie et al_2019_Micro-, meso-, macroscales.pdf;/home/aleksandar/Zotero/storage/FJFSGKDH/PhysRevE.100.html;/home/aleksandar/Zotero/storage/LDG49G86/PhysRevE.100.html},
  journal = {Physical Review E},
  number = {2}
}

@article{WhatCostVoting,
  title = {What Is the Cost of Voting?},
  doi = {10.2307/j.ctt5hjrrf.8},
  abstract = {{$<$}div{$><$}h2{$>$}Abstract{$<$}/h2{$><$}p{$>$}Despite a wealth of literature on the determinants of electoral turnout, little is known about the cost of voting. Some studies suggest that facilitating voting slightly increases turnout, but what ultimately matters is people's subjective perceptions of how costly voting is. This paper offers a first comprehensive analysis of the {$<$}em{$>$}subjective{$<$}/em{$>$} cost of voting and its impact on voter turnout. We use data from an original survey conducted in Canada and data from the Making Electoral Democracy Work project which covers 23 elections among 5 different countries. We distinguish direct and information/decision voting costs. That is, the direct costs that are related to the act of voting and the costs that are related to the efforts to make (an informed) choice. We find that the cost of voting is generally perceived to be very small but that those who find voting more difficult are indeed less prone to vote, controlling for a host of other considerations. That impact, however, is relatively small, and the direct cost matters more than the information/decision cost.{$<$}/p{$><$}/div{$>$}},
  journal = {Electoral Studies}
}

@article{WhatCostVotinga,
  title = {What Is the Cost of Voting?},
  doi = {10.2307/j.ctt5hjrrf.8},
  abstract = {{$<$}div{$><$}h2{$>$}Abstract{$<$}/h2{$><$}p{$>$}Despite a wealth of literature on the determinants of electoral turnout, little is known about the cost of voting. Some studies suggest that facilitating voting slightly increases turnout, but what ultimately matters is people's subjective perceptions of how costly voting is. This paper offers a first comprehensive analysis of the {$<$}em{$>$}subjective{$<$}/em{$>$} cost of voting and its impact on voter turnout. We use data from an original survey conducted in Canada and data from the Making Electoral Democracy Work project which covers 23 elections among 5 different countries. We distinguish direct and information/decision voting costs. That is, the direct costs that are related to the act of voting and the costs that are related to the efforts to make (an informed) choice. We find that the cost of voting is generally perceived to be very small but that those who find voting more difficult are indeed less prone to vote, controlling for a host of other considerations. That impact, however, is relatively small, and the direct cost matters more than the information/decision cost.{$<$}/p{$><$}/div{$>$}},
  journal = {Electoral Studies}
}

@article{wheelerIdeologyPredictiveProcessing2020,
  title = {Ideology and Predictive Processing: Coordination, Bias, and Polarization in Socially Constrained Error Minimization},
  shorttitle = {Ideology and Predictive Processing},
  author = {Wheeler, Nathan E and Allidina, Suraiya and Long, Elizabeth U and Schneider, Stephen P and Haas, Ingrid J and Cunningham, William A},
  year = {2020},
  month = aug,
  volume = {34},
  pages = {192--198},
  issn = {2352-1546},
  doi = {10.1016/j.cobeha.2020.05.002},
  abstract = {Recent models of cognition suggest that the brain may implement predictive processing, in which top-down expectations constrain incoming sensory data. In this perspective, expectations are updated (error minimization) only if sensory data sufficiently deviate from these expectations (prediction error). Although originally applied to perception, predictive processing is thought to generally characterize cognitive architecture, including the social cognitive processes involved in ideological thinking. Scaling up these simple computational principles to the social sphere outlines a path by which group members may adopt shared ideologies and beliefs to predict behavior and cooperate with each other. Because ideological judgments are of specific interest to others in our political groups, we may increasingly regulate each other's thinking, sharing the process of error minimization. In this paper, we outline how this process of shared error minimization may lead to shared ideologies and beliefs that allow group members to predict and cooperate with each other, and how, as a consequence, political polarization and extremism may result.},
  file = {/home/aleksandar/Zotero/storage/WVXPEFGX/S2352154620300632.html},
  journal = {Current Opinion in Behavioral Sciences},
  language = {en}
}

@article{whiteheadHomosexualityReligionScience2012,
  title = {Homosexuality, {{Religion}}, and {{Science}}: {{Moral Authority}} and the {{Persistence}} of {{Negative Attitudes}}*},
  shorttitle = {Homosexuality, {{Religion}}, and {{Science}}},
  author = {Whitehead, Andrew L. and Baker, Joseph O.},
  year = {2012},
  volume = {82},
  pages = {487--509},
  issn = {1475-682X},
  doi = {10.1111/j.1475-682X.2012.00425.x},
  abstract = {The liberalization of attitudes toward homosexuality in the United States over the past 30 years is well documented. Despite these changes, substantial resistance to equality for gay men and lesbians remains. Previous studies indicate that beliefs about the etiology of homosexuality are central to this discussion. Those who believe homosexuality is innate are more favorable, while those who believe it is the result of a choice are more negative. Moreover, experimental research indicates that those with negative views actually become more opposed when a natural explanation is proposed. This study highlights the importance of perceived sources of epistemic and moral authority for understanding views of homosexuality. Using stances on culturally controversial issues involving ``science and religion'' as indicators of where individuals place authority, we outline the connection between perceptions of moral authority and attributions about homosexuality. Analyses of a national survey of American adults show that, net of controls, one's stance on moral authority is the strongest predictor of attributions about whether homosexuality is chosen or innate.},
  copyright = {\textcopyright{} 2012 Alpha Kappa Delta},
  file = {/home/aleksandar/Zotero/storage/KDXNN2NT/j.1475-682X.2012.00425.html},
  journal = {Sociological Inquiry},
  language = {en},
  number = {4}
}

@techreport{whiteMentalModelsSupernaturallyenforced2019,
  title = {Mental Models of Supernaturally-Enforced Morality: {{Associations}} between Beliefs about Karma and {{God}}, Moral Values, and Prosocial Behavior},
  shorttitle = {Mental Models of Supernaturally-Enforced Morality},
  author = {White, Cindel and Norenzayan, Ara},
  year = {2019},
  month = dec,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/a7398},
  abstract = {Cultural evolutionary theories have proposed that prosocial religious traditions can facilitate societal complexity and large-scale cooperation among strangers, in part, through the culturally transmitted commitment to morally concerned supernatural entities. Although a growing number of studies have documented an association between reminders of religious concepts and increased prosocial behavior, fewer studies have directly examined mental models of supernaturally monitored morality. Study 1 used pre-existing closed-ended questionnaires to assess whether beliefs about karma reflect one's secular moral values. Studies 2 and 3 used an open-ended free-list task to investigate participants' mental models of which human behaviors are rewarded and punished by God and karma, among samples from the USA and India, including Hindu, Buddhist, Christian, and non-religious participants. We investigate how mental models of God and karma's moral concerns reflect (1) widespread consensus about the relevance of interpersonal cooperation, (2) one's own moral values and internalized cultural norms for moral behavior, and (3) target-specific differences between mental models of God and karma. Results demonstrate how supernatural beliefs reflect aspects of individuals' secular, interpersonal moral values, while also containing unique elements due to the particular relationships that believers have different supernatural entities.},
  file = {/home/aleksandar/Dropbox/Zotero/White_Norenzayan/White_Norenzayan_2019_Mental models of supernaturally-enforced morality.pdf},
  language = {en},
  type = {Preprint}
}

@article{whiteSocialStructureMultiple1976,
  title = {Social {{Structure}} from {{Multiple Networks}}. {{I}}. {{Blockmodels}} of {{Roles}} and {{Positions}}},
  author = {White, Harrison C. and Boorman, Scott A. and Breiger, Ronald L.},
  year = {1976},
  month = jan,
  volume = {81},
  pages = {730--780},
  issn = {0002-9602, 1537-5390},
  doi = {10.1086/226141},
  journal = {American Journal of Sociology},
  language = {en},
  number = {4}
}

@misc{WhyCitizensNot,
  title = {Why {{Do Citizens Not Turn Out}}? {{The Effect}} of {{Election}}-{{Specific Knowledge}} on {{Turnout}} in {{European Elections}} in {{Eastern Europe}} - {{Vanja Petri\v{c}evi\'c}}, {{Daniel Stockemer}},},
  file = {/home/aleksandar/Zotero/storage/8IIL8RFA/0888325419870228.html},
  howpublished = {https://journals.sagepub.com/doi/abs/10.1177/0888325419870228}
}

@book{wickhamGgplot2ElegantGraphics2016,
  title = {Ggplot2: {{Elegant}} Graphics for Data Analysis},
  author = {Wickham, Hadley},
  year = {2016},
  publisher = {{Springer-Verlag}},
  address = {{New York}},
  isbn = {978-3-319-24277-4}
}

@article{widfeldtPopulistPartiesEurope2016,
  title = {Populist Parties in {{Europe}}: {{Agents}} of Discontent?},
  shorttitle = {Populist {{Parties}} in {{Europe}}},
  author = {Widfeldt, Anders},
  year = {2016},
  month = jul,
  volume = {39},
  pages = {907--908},
  issn = {0140-2382, 1743-9655},
  doi = {10.1080/01402382.2016.1166737},
  journal = {West European Politics},
  language = {en},
  number = {4}
}

@article{wiesmannEffectChloroquineCultured1975,
  title = {Effect of Chloroquine on Cultured Fibroblasts: Release of Lysosomal Hydrolases and Inhibition of Their Uptake},
  shorttitle = {Effect of Chloroquine on Cultured Fibroblasts},
  author = {Wiesmann, U. N. and DiDonato, S. and Herschkowitz, N. N.},
  year = {1975},
  month = oct,
  volume = {66},
  pages = {1338--1343},
  issn = {1090-2104},
  journal = {Biochemical and Biophysical Research Communications},
  language = {eng},
  number = {4},
  pmid = {4}
}

@techreport{williamsBGGMBayesianGaussian2020,
  title = {{{BGGM}}: {{Bayesian Gaussian Graphical Models}} in {{R}}},
  shorttitle = {{{BGGM}}},
  author = {Williams, Donald Ray and Mulder, Joris},
  year = {2020},
  month = jan,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/t2cn7},
  abstract = {BGGM is built around two approaches for Bayesian inference\textendash estimation and hypothesistesting. This distinction is arbitrary (see Rouder, Haaf, \& Vandekerckhove, 2018), but isused to organize this work. The former focuses on the posterior distribution and includesextensions to assess predictability (Haslbeck \& Waldorp, 2018), as well as methodology tocompare partial correlations. The latter includes methods for Bayesian hypothesis testing,in both exploratory and confirmatory contexts, with the novel matrix-F prior distribution(Mulder \& Pericchi, 2018). This allows for testing the null hypothesis of conditionalindependence, as well as inequality and equality constrained hypotheses. Further, thereare several approaches for comparing GGMs across any number of groups. The packagealso includes a suite of options for model checking. Together, BGGM is a comprehensivetoolbox for Gaussian graphical modeling in R.},
  language = {en},
  type = {Preprint}
}

@article{williamsComparingGaussianGraphical,
  title = {Comparing {{Gaussian Graphical Models}} with the {{Posterior Predictive Distribution}} and {{Bayesian Model Selection}}},
  author = {Williams, Donald R and Pericchi, Luis R and Rast, Philippe and Mulder, Joris},
  pages = {22},
  file = {/home/aleksandar/Dropbox/Zotero/Williams et al/Williams et al_Comparing Gaussian Graphical Models with the Posterior Predictive Distribution.pdf},
  language = {en}
}

@techreport{williamsGGMncvNonconvexPenalized2020,
  title = {{{GGMncv}}: {{Nonconvex Penalized Gaussian Graphical Models}} in {{R}}},
  shorttitle = {{{GGMncv}}},
  author = {Williams, Donald Ray},
  year = {2020},
  month = nov,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/6jz5m},
  abstract = {Studying complex relations in multivariate datasets is a common task across the sciences. Recently, the Gaussian graphical model has emerged as an increasingly popular model for characterizing the conditional dependence structure of random variables. Although the graphical lasso ( 1-regularization) is the most well-known estimator, it has several drawbacks that make it less than ideal for model selection. There are now alternative forms of regularization that were developed specifically to overcome issues inherent to the 1-penalty. To date, however, these alternatives have been slow to work their way into software for research workers. To address this dearth of software, I developed the package GGMncv that includes a variety of nonconvex penalties, two algorithms for their estimation, plotting capabilities, and an approach for making statistical inference. As an added bonus, GGMncv can be used for nonconvex penalized least squares. After describing the various nonconvex penalties, the functionality of GGMncv is demonstrated through examples using a dataset from personality psychology.},
  file = {/home/aleksandar/Zotero/storage/EGAINHKU/Williams - 2020 - GGMncv Nonconvex Penalized Gaussian Graphical Mod.pdf},
  language = {en},
  type = {Preprint}
}

@techreport{williamsLassoSurveyNonconvex2020,
  title = {Beyond {{Lasso}}: {{A Survey}} of {{Nonconvex Regularization}} in {{Gaussian Graphical Models}}},
  shorttitle = {Beyond {{Lasso}}},
  author = {Williams, Donald Ray},
  year = {2020},
  month = nov,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/ad57p},
  abstract = {Studying complex relations in multivariate datasets is a common task in psychological science. Recently, the Gaussian graphical model has emerged as an increasingly popular model for characterizing the conditional dependence structure of random variables. Although the graphical lasso ( 1-regularization) is the most well-known estimator across the sciences, it has several drawbacks that make it less than ideal for model selection. There are now alternative forms of regularization that were developed specifically to overcome issues inherent to the 1-penalty. To date, this information has not been synthesized. This paper provides a comprehensive survey of nonconvex regularization that spans from the smoothly clipped absolute deviation penalty to continuous approximations of the 0-penalty (i.e., best subset) for directly estimating the inverse covariance matrix. A common thread shared by these penalties is that they all enjoy the oracle properties, that is, they perform as though the true generating model were known in advance. To ensure their theoretical properties are general, I conducted extensive numerical experiments that indicated superior and more than competitive performance when compared to glasso and non-regularized model selection, respectively, all the while being computationally feasible for many variables. In addition, the important topics of tuning parameter selection and statistical inference in regularized models are reviewed. The penalties are employed to estimate the dependence structure of post-traumatic stress disorder symptoms. The discussion includes several ideas for future research, including a plethora of information to facilitate their study. I have implemented the methods in the R package GGMncv.},
  file = {/home/aleksandar/Zotero/storage/UXY9XGGQ/Williams - 2020 - Beyond Lasso A Survey of Nonconvex Regularization.pdf},
  language = {en},
  type = {Preprint}
}

@techreport{williamsLearningLiveSampling2020,
  title = {Learning to {{Live}} with {{Sampling Variability}}: {{Expected Replicability}} in {{Partial Correlation Networks}}},
  shorttitle = {Learning to {{Live}} with {{Sampling Variability}}},
  author = {Williams, Donald Ray},
  year = {2020},
  month = jan,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/fb4sa},
  abstract = {The topic of replicability has recently captivated the emerging field of network psychometrics. Although methodological practice (e.g., p-hacking) has been identified as a root cause of unreliable research findings in psychological science, the statistical model itself has come under attack in the partial correlation network literature. In a motivating example, I first describe how sampling variability inherent to partial correlations can merely give the appearance of unreliability. For example, when going from zero-order to partial correlations there is necessarily more sampling variability that translates into reduced statistical power. I then introduce novel methodology for deriving expected network replicability. This analytic solution can be used with Pearson, Spearman, and polychoric partial correlations. I employ the method to highlight an additional source of sampling variability, that is, when going from continuous to ordinal data with few categories: in networks with 20 variables (N = 500) replicability can exceed 50\% for continuous data but this decreases to less than 25\% for ordinal data! Additionally, I propose using the smallest edge size of interest to achieve a desired level of replicability in network models. I end with recommendations that include the importance of the network literature repositioning itself with gold-standard approaches for assessing replication (e.g., by using methods with defined error rates). I have implemented the method for computing expected network replicability in the R package GGMnonreg.},
  file = {/home/aleksandar/Dropbox/Zotero/Williams/Williams_2020_Learning to Live with Sampling Variability.pdf;/home/aleksandar/Dropbox/Zotero/Williams/Williams_2020_Learning to Live with Sampling Variability2.pdf},
  language = {en},
  type = {Preprint}
}

@techreport{williamsLearningLiveSampling2020a,
  title = {Learning to {{Live}} with {{Sampling Variability}}: {{Expected Replicability}} in {{Partial Correlation Networks}}},
  shorttitle = {Learning to {{Live}} with {{Sampling Variability}}},
  author = {Williams, Donald Ray},
  year = {2020},
  month = jan,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/fb4sa},
  abstract = {The topic of replicability has recently captivated the emerging field of network psychometrics. Although methodological practice (e.g., p-hacking) has been identified as a root cause of unreliable research findings in psychological science, the statistical model itself has come under attack in the partial correlation network literature. In a motivating example, I first describe how sampling variability inherent to partial correlations can merely give the appearance of unreliability. For example, when going from zero-order to partial correlations there is necessarily more sampling variability that translates into reduced statistical power. I then introduce novel methodology for deriving expected network replicability. This analytic solution can be used with Pearson, Spearman, and polychoric partial correlations. I employ the method to highlight an additional source of sampling variability, that is, when going from continuous to ordinal data with few categories: in networks with 20 variables (N = 500) replicability can exceed 50\% for continuous data but this decreases to less than 25\% for ordinal data! Additionally, I propose using the smallest edge size of interest to achieve a desired level of replicability in network models. I end with recommendations that include the importance of the network literature repositioning itself with gold-standard approaches for assessing replication (e.g., by using methods with defined error rates). I have implemented the method for computing expected network replicability in the R package GGMnonreg.},
  language = {en},
  type = {Preprint}
}

@techreport{williamsWhyOverfittingNot2020,
  title = {Why {{Overfitting}} Is {{Not}} ({{Usually}}) a {{Problem}} in {{Partial Correlation Networks}}},
  author = {Williams, Donald Ray and Rodriguez, Josue E.},
  year = {2020},
  month = mar,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/8pr9b},
  abstract = {Network psychometrics is undergoing a time of methodological reflection. In part, this was spurred by the revelation that 1-regularization does not reduce spurious associations in partial correlation networks. In this work, we address another motivation for the widespread use of regularized estimation: the thought that it is needed to mitigate overfitting. We first clarify important aspects of overfitting and the bias-variance tradeoff that are especially relevant for the network literature, where the number of nodes or items in a psychometric scale are not large compared to the number of observations (i.e., a low p/n ratio). This revealed that bias and especially variance are most problematic in p/n ratios rarely encountered. We then introduce a nonregularized method, based on classical hypothesis testing, that fulfills two desiderata: (1) reducing or controlling the false positives rate and (2) quelling concerns of overfitting by providing accurate predictions. These were the primary motivations for initially adopting the graphical lasso (glasso). In several simulation studies, our nonregularized method provided more than competitive predictive performance, and, in many cases, outperformed glasso. It appears to be nonregularized, as opposed to regularized estimation, that best satisfies these desiderata. We then provide insights into using our methodology. Here we discuss the multiple comparisons problem in relation to prediction: stringent alpha levels, resulting in a sparse network, can deteriorate predictive accuracy. We end by emphasizing key advantages of our approach that make it ideal for both inference and prediction in network analysis.},
  file = {/home/aleksandar/Dropbox/Zotero/Williams_Rodriguez/Williams_Rodriguez_2020_Why Overfitting is Not (Usually) a Problem in Partial Correlation Networks.pdf;/home/aleksandar/Dropbox/Zotero/Williams_Rodriguez/Williams_Rodriguez_2020_Why Overfitting is Not (Usually) a Problem in Partial Correlation Networks2.pdf},
  language = {en},
  type = {Preprint}
}

@article{wilmsDynamicProgrammingAlgorithms,
  title = {Dynamic Programming Algorithms for Computing Power Indices in Weighted Multi-Tier Games},
  author = {Wilms, Ingo},
  doi = {10.1016/j.mathsocsci.2020.06.004},
  abstract = {{$<$}p{$>$}In weighted games each voter has a weight assigned and "yes"-voters win if the sum of each weight is greater or equal to the quota. In weighted multi-tier games, we have several weighted games (tiers) over the same set of voters. In this article, algorithms for calculating Banzhaf and Shapley-Shubik indices for three different type of games are proposed: weighted AND-games, weighted OR-games and games where the tiers have conjunctive normal form. The presented algorithms are generalizations of known computational methods using dynamic programming technique. Finally, some applications and experiments are carried out and these algorithms are compared to a fairly new method based on binary decision diagrams.{$<$}/p{$>$}},
  journal = {Mathematical Social Science}
}

@article{wiltshireSocialAnalyticsTeam2020,
  title = {Social {{Analytics}} of {{Team Interaction}} Using {{Dynamic Complexity Heat Maps}} and {{Network Visualizations}}},
  author = {Wiltshire, Travis J. and Hudson, Dan and Lijdsman, Philia and Wever, Stijn and Atzmueller, Martin},
  year = {2020},
  month = sep,
  abstract = {Given the increasing complexity of many sociotechnical work domains, effective teamwork is becoming more and more crucial. While face-to-face communication contributes to effective teamwork, understanding the time-varying nature and structure of team communication is limited. In this work, we combine sensor-based social analytics of Socio-metric badges (Rhythm Badge) with two visualization techniques (Dynamic Complexity Heat Maps and Network Visualizations) in order to advance an intuitive way of understanding the dynamics of team interaction. To demonstrate the utility of our approach, we provide a case study that examines one team's interaction for a Lost at Sea simulation. Our results show that we are able to recover transitions in the task and in the team interaction. Not only this, but we uncover structural changes in the energy and engagement of team members and visualize these in networks. Taken together, this work represents an important first step at identifying critical transitions/events in team communication and interaction patterns that form the basis for many promising future studies focused on optimizing team performance and well-being.},
  archivePrefix = {arXiv},
  eprint = {2009.04445},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Zotero/storage/B8PGZZJN/Wiltshire et al_2020_Social Analytics of Team Interaction using Dynamic Complexity Heat Maps and.pdf;/home/aleksandar/Zotero/storage/7TJFYQB5/2009.html},
  journal = {arXiv:2009.04445 [cs]},
  primaryClass = {cs}
}

@article{wingWhatCanInstrumental,
  ids = {wingWhatCanInstrumentala},
  title = {What {{Can Instrumental Variables Tell Us About Nonresponse In Household Surveys}} and {{Political Polls}}?},
  author = {Wing, Coady},
  doi = {10.1017/pan.2018.58},
  abstract = {This paper introduces an instrumental variables framework for analyzing how external factors that affect survey response rates can also affect the composition of the sample of respondents. The method may be useful for studying survey representativeness, and for assessing the effectiveness of some of the conventional corrections for survey nonresponse bias. The paper applies the method to data collected in the 2011 Swiss Electoral Study (SES), in which survey participation incentives were randomly assigned across members of the original survey sample. The empirical analysis shows that the incentives increased response rates substantially. Estimates of a new instrumental variable parameter called the Complier Average Survey Response (CASR) suggest that the incentives induced participation among people with more nationalist political opinions than those who would have participated without the incentives. Weighting the respondent data to match the covariate distribution in the target population did not account for the discrepancy in attitudes between the two groups, suggesting that the weights would not succeed in removing nonresponse bias.},
  journal = {Political Analysis}
}

@article{winkelmannSocialTippingProcesses2020,
  title = {Social Tipping Processes for Sustainability: {{An}} Analytical Framework},
  shorttitle = {Social Tipping Processes for Sustainability},
  author = {Winkelmann, Ricarda and Donges, Jonathan F. and Smith, E. Keith and Milkoreit, Manjana and Eder, Christina and Heitzig, Jobst and Katsanidou, Alexia and Wiedermann, Marc and Wunderling, Nico and Lenton, Timothy M.},
  year = {2020},
  month = oct,
  abstract = {Societal transformations are necessary to address critical global challenges, such as mitigation of anthropogenic climate change and reaching UN sustainable development goals. Recently, social tipping processes have received increased attention, as they present a form of social change whereby a small change can shift a sensitive social system into a qualitatively different state due to strongly self-amplifying (mathematically positive) feedback mechanisms. Social tipping processes have been suggested as key drivers of sustainability transitions emerging in the fields of technological and energy systems, political mobilization, financial markets and sociocultural norms and behaviors. Drawing from expert elicitation and comprehensive literature review, we develop a framework to identify and characterize social tipping processes critical to facilitating rapid social transformations. We find that social tipping processes are distinguishable from those of already more widely studied climate and ecological tipping dynamics. In particular, we identify human agency, social-institutional network structures, different spatial and temporal scales and increased complexity as key distinctive features underlying social tipping processes. Building on these characteristics, we propose a formal definition for social tipping processes and filtering criteria for those processes that could be decisive for future trajectories to global sustainability in the Anthropocene. We illustrate this definition with the European political system as an example of potential social tipping processes, highlighting the potential role of the FridaysForFuture movement. Accordingly, this analytical framework for social tipping processes can be utilized to illuminate mechanisms for necessary transformative climate change mitigation policies and actions.},
  archivePrefix = {arXiv},
  eprint = {2010.04488},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Winkelmann et al/Winkelmann et al_2020_Social tipping processes for sustainability.pdf;/home/aleksandar/Zotero/storage/GFU3MBN4/2010.html},
  journal = {arXiv:2010.04488 [physics]},
  primaryClass = {physics}
}

@article{winterMultiscaleGraphPrincipal2020,
  title = {Multi-Scale Graph Principal Component Analysis for Connectomics},
  author = {Winter, Steven and Zhang, Zhengwu and Dunson, David},
  year = {2020},
  month = oct,
  abstract = {In brain connectomics, the cortical surface is parcellated into different regions of interest (ROIs) prior to statistical analysis. The brain connectome for each individual can then be represented as a graph, with the nodes corresponding to ROIs and edges to connections between ROIs. Such a graph can be summarized as an adjacency matrix, with each cell containing the strength of connection between a pair of ROIs. These matrices are symmetric with the diagonal elements corresponding to self-connections typically excluded. A major disadvantage of such representations of the connectome is their sensitivity to the chosen ROIs, including critically the number of ROIs and hence the scale of the graph. As the scale becomes finer and more ROIs are used, graphs become increasingly sparse. Clearly, the results of downstream statistical analyses can be highly dependent on the chosen parcellation. To solve this problem, we propose a multi-scale graph factorization, which links together scale-specific factorizations through a common set of individual-specific scores. These scores summarize an individual's brain structure combining information across measurement scales. We obtain a simple and efficient algorithm for implementation, and illustrate substantial advantages over single scale approaches in simulations and analyses of the Human Connectome Project dataset.},
  archivePrefix = {arXiv},
  eprint = {2010.02332},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Winter et al/Winter et al_2020_Multi-scale graph principal component analysis for connectomics.pdf;/home/aleksandar/Zotero/storage/4DS856NX/2010.html},
  journal = {arXiv:2010.02332 [stat]},
  primaryClass = {stat}
}

@misc{WiringIntelligenceAlexander,
  title = {The {{Wiring}} of {{Intelligence}} - {{Alexander O}}. {{Savi}}, {{Maarten Marsman}}, {{Han L}}. {{J}}. van Der {{Maas}}, {{Gunter K}}. {{J}}. {{Maris}}, 2019},
  file = {/home/aleksandar/Zotero/storage/SD28GKDG/1745691619866447.html},
  howpublished = {https://journals.sagepub.com/doi/full/10.1177/1745691619866447}
}

@misc{WorldviewConflictDaily,
  title = {Worldview {{Conflict}} in {{Daily Life}} - {{Mark J}}. {{Brandt}}, {{Jarret T}}. {{Crawford}}, {{Daryl R}}. {{Van Tongeren}}, 2019},
  file = {/home/aleksandar/Zotero/storage/VKBLARXF/1948550617733517.html},
  howpublished = {https://journals.sagepub.com/doi/10.1177/1948550617733517}
}

@article{wuAgentbasedSimulationsChina,
  title = {Agent-Based Simulations of {{China}} Inbound Tourism Network.},
  author = {Wu, Jinfeng and Wang, Xingang and Pan, Bing},
  doi = {arXiv:1901.00080v1},
  abstract = {{$<$}p{$>$}Based on the results of a large-scale survey, we construct an agent-based network model for the independent inbound tourism of China and, by the approach of numerical simulation, investigate the dynamical responses of the tourist flows to external perturbations in different scenarios, including the closure of a tourist city, the opening of a new port in western China, and the increase of the tourism attractiveness of a specific city. Numerical results show that: (1) the closure of a single city in general will affect the tourist visitations of many other cities in the network and, comparing to the non-port cities, the overall visitation volume of the system is more influenced by closing a port city; (2) the opening of a new port city in western China will attract more tourists to the western cities, but has a negligible impact on either the overall visitation volume or the imbalanced tourist distribution; and (3) the increase of the tourism attractiveness of a non-port (port) city normally increases (decreases) the overall visitation volume, yet there are exceptions due to the spillover effect. Furthermore, by increasing the tourism attractiveness of a few cities simultaneously, we investigate also the strategy of multiple-city-upgrade in tourism development. Numerical results show that the overall tourist volume is better improved by upgrading important non-port cities that are geographically distant from each other. The study reveals the rich dynamics inherent in complex tourism network, and the findings could be helpful to the development and management of China inbound tourism. {$<$}/p{$>$}},
  journal = {arXiv Physics General}
}

@article{wuCorrelatedStructuralEvolution,
  title = {Correlated Structural Evolution within Multiplex Networks},
  author = {Wu, Haochen and James, Ryan G and D'Souza, Raissa M},
  doi = {10.1093/comnet/cnaa014},
  abstract = {Many natural, engineered and social systems can be represented using the framework of a layered network, where each layer captures a different type of interaction between the same set of nodes. The study of such {$<$}em{$>$}multiplex networks{$<$}/em{$>$} is a vibrant area of research. Yet, understanding how to quantify the correlations present between pairs of layers, and more so present in their co-evolution, is lacking. Such methods would enable us to address fundamental questions involving issues such as function, redundancy, and potential disruptions. Here, we show first how the edge set of a multiplex network can be used to construct an estimator of a joint probability distribution describing edge existence over all layers. We then adapt an information-theoretic measure of general correlation called the conditional mutual information, which uses the estimated joint probability distribution, to quantify the pairwise correlations present between layers. The pairwise comparisons can also be temporal, allowing us to identify if knowledge of a certain layer can provide additional information about the evolution of another layer. We analyse datasets from three distinct domains\textemdash economic, political, and airline networks\textemdash to demonstrate how pairwise correlation in structure and dynamical evolution between layers can be identified and show that anomalies can serve as potential indicators of major events such as shocks.},
  journal = {Journal of Complex Networks}
}

@article{wuDistributedCommunityDetection2020,
  title = {Distributed {{Community Detection}} for {{Large Scale Networks Using Stochastic Block Model}}},
  author = {Wu, Shihao and Li, Zhe and Zhu, Xuening},
  year = {2020},
  month = sep,
  abstract = {With rapid developments of information and technology, large scale network data are ubiquitous. In this work we develop a distributed spectral clustering algorithm for community detection in large scale networks. To handle the problem, we distribute l pilot network nodes on the master server and the others on worker servers. A spectral clustering algorithm is first conducted on the master to select pseudo centers. The indexes of the pseudo centers are then broadcasted to workers to complete distributed community detection task using a SVD type algorithm. The proposed distributed algorithm has three merits. First, the communication cost is low since only the indexes of pseudo centers are communicated. Second, no further iteration algorithm is needed on workers and hence it does not suffer from problems as initialization and non-robustness. Third, both the computational complexity and the storage requirements are much lower compared to using the whole adjacency matrix. A Python package DCD (www.github.com/Ikerlz/dcd) is developed to implement the distributed algorithm for a Spark system. Theoretical properties are provided with respect to the estimation accuracy and mis-clustering rates. Lastly, the advantages of the proposed methodology are illustrated by experiments on a variety of synthetic and empirical datasets.},
  archivePrefix = {arXiv},
  eprint = {2009.11747},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Wu et al/Wu et al_2020_Distributed Community Detection for Large Scale Networks Using Stochastic Block.pdf},
  journal = {arXiv:2009.11747 [stat]},
  primaryClass = {stat}
}

@article{wulkowMemorybasedReducedModelling2020,
  title = {Memory-Based Reduced Modelling and Data-Based Estimation of Opinion Spreading},
  author = {Wulkow, Niklas and Koltai, P{\'e}ter and Sch{\"u}tte, Christof},
  year = {2020},
  month = jun,
  abstract = {We investigate opinion dynamics based on an agent-based model, and are interested in predicting the evolution of the percentages of the entire agent population that share an opinion. Since these opinion percentages can be seen as an aggregated observation of the full system state, the individual opinions of each agent, we view this in the framework of the Mori-Zwanzig projection formalism. More specifically, we show how to estimate a nonlinear autoregressive model (NAR) with memory from data given by a time series of opinion percentages, and discuss its prediction capacities for various specific topologies of the agent interaction network. We demonstrate that the inclusion of memory terms significantly improves the prediction quality on examples with different network topologies.},
  archivePrefix = {arXiv},
  eprint = {2006.08257},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Dropbox/Zotero/Wulkow et al/Wulkow et al_2020_Memory-based reduced modelling and data-based estimation of opinion spreading.pdf;/home/aleksandar/Zotero/storage/V62K7VDW/2006.html},
  journal = {arXiv:2006.08257 [math]},
  primaryClass = {math}
}

@article{wuttkeHaveEuropeansGrown2020,
  title = {Have {{Europeans Grown Tired}} of {{Democracy}}? {{New Evidence}} from {{Eighteen Consolidated Democracies}}, 1981\textendash 2018},
  shorttitle = {Have {{Europeans Grown Tired}} of {{Democracy}}?},
  author = {Wuttke, Alexander and Gavras, Konstantin and Schoen, Harald},
  year = {2020},
  month = oct,
  pages = {1--13},
  issn = {0007-1234, 1469-2112},
  doi = {10.1017/S0007123420000149},
  abstract = {Democracies without democrats are not sustainable. Yet, recent studies have argued that Western citizens are turning their backs on the system of self-governance, thereby eroding the societal foundations of consolidated democracies. This study contributes to discussions about citizen support of democracy by (1) analyzing new cross-national survey data in 18 European countries that facilitate assessments of the temporal and geographical generalizability of previous findings, (2) disentangling age, cohort and period effects, thereby aligning the analytical methods with the theoretical arguments and (3) transparently reporting all evidence derived from pre-registered analyses to avoid cherry-picked findings. The findings show that citizens of consolidated democracies continue to endorse self-governance. Yet in some (but not all) countries, there is evidence of a growing number of `democrats in name only', particularly among the young generation. These findings suggest a second phase in research on democratic fatigue that broadens the analytical scope for the multi-faceted nature of democratic support.},
  file = {/home/aleksandar/Zotero/storage/E4K94HV9/Wuttke et al. - 2020 - Have Europeans Grown Tired of Democracy New Evide.pdf},
  journal = {British Journal of Political Science},
  language = {en}
}

@article{wysockiPenaltyParameterSelection2019,
  ids = {wysockiPenaltyParameterSelection,wysockiPenaltyParameterSelectiona,wysockiPenaltyParameterSelectionb},
  title = {On {{Penalty Parameter Selection}} for {{Estimating Network Models}}},
  author = {Wysocki, Anna C. and Rhemtulla, Mijke},
  year = {2019},
  month = nov,
  volume = {0},
  pages = {1--15},
  issn = {0027-3171},
  doi = {10.1080/00273171.2019.1672516},
  abstract = {Network models are gaining popularity as a way to estimate direct effects among psychological variables and investigate the structure of constructs. A key feature of network estimation is determining which edges are likely to be non-zero. In psychology, this is commonly achieved through the graphical lasso regularization method that estimates a precision matrix of Gaussian variables using an {$\mathscr{l}$}1-penalty to push small values to zero. A tuning parameter, {$\lambda$}, controls the sparsity of the network. There are many methods to select {$\lambda$}, which can lead to vastly different graphs. The most common approach in psychological network applications is to minimize the extended Bayesian information criterion, but the consistency of this method for model selection has primarily been examined in high dimensional settings (i.e., n {$<$} p) that are uncommon in psychology. Further, there is some evidence that alternative selection methods may have superior performance. Here, using simulation, we compare four different methods for selecting {$\lambda$}, including the stability approach to regularization selection (StARS), K-fold cross-validation, the rotation information criterion (RIC), and the extended Bayesian information criterion (EBIC). Our results demonstrate that penalty parameter selection should be made based on data characteristics and the inferential goal (e.g., to increase sensitivity versus to avoid false positives). We end with recommendations for selecting the penalty parameter when using the graphical lasso.},
  file = {/home/aleksandar/Dropbox/Zotero/Wysocki_Rhemtulla/Wysocki_Rhemtulla_2019_On Penalty Parameter Selection for Estimating Network Models.pdf},
  journal = {Multivariate Behavioral Research},
  number = {0},
  pmid = {31672065}
}

@inproceedings{xiangModelingRelationshipStrength2010,
  title = {Modeling Relationship Strength in Online Social Networks},
  booktitle = {Proceedings of the 19th International Conference on {{World}} Wide Web},
  author = {Xiang, Rongjing and Neville, Jennifer and Rogati, Monica},
  year = {2010},
  month = apr,
  pages = {981--990},
  publisher = {{Association for Computing Machinery}},
  address = {{New York, NY, USA}},
  doi = {10.1145/1772690.1772790},
  abstract = {Previous work analyzing social networks has mainly focused on binary friendship relations. However, in online social networks the low cost of link formation can lead to networks with heterogeneous relationship strengths (e.g., acquaintances and best friends mixed together). In this case, the binary friendship indicator provides only a coarse representation of relationship information. In this work, we develop an unsupervised model to estimate relationship strength from interaction activity (e.g., communication, tagging) and user similarity. More specifically, we formulate a link-based latent variable model, along with a coordinate ascent optimization procedure for the inference. We evaluate our approach on real-world data from Facebook and LinkedIn, showing that the estimated link weights result in higher autocorrelation and lead to improved classification accuracy.},
  file = {/home/aleksandar/Dropbox/Zotero/Xiang et al/Xiang et al_2010_Modeling relationship strength in online social networks.pdf},
  isbn = {978-1-60558-799-8},
  keywords = {DONE},
  series = {{{WWW}} '10}
}

@article{xiaoManagingClassificationbasedConsensus2020,
  title = {Managing Classification-Based Consensus in Social Network Group Decision Making: {{An}} Optimization-Based Approach with Minimum Information Loss},
  shorttitle = {Managing Classification-Based Consensus in Social Network Group Decision Making},
  author = {Xiao, Jing and Wang, Xiuli and Zhang, Hengjie},
  year = {2020},
  month = jun,
  issn = {1566-2535},
  doi = {10.1016/j.inffus.2020.05.008},
  abstract = {This study proposes a classification-based consensus framework in social network group decision making, which aims to classify alternatives into several ordinal classes from best to worst. In the classification-based consensus framework, a maximum consensus-based optimization model is devised to determine the weight of decision makers by linearly combining three reliable sources: in-degree centrality, consistency and similarity indexes. This is done by maximizing the consensus level among decision makers regarding the collective classification of alternatives. Following this, a minimum information loss-based optimization model is constructed to generate the consensual collective classification of alternatives. It seeks to minimize the information loss between the additive preference relations provided by decision makers and their preference vectors. Particularly, the proposed optimization models are converted into 0-1 mixed linear programming models to easily find their optimal solutions. Finally, a numerical example and a detailed comparison analysis are provided to show the effectiveness of the proposed approach.},
  file = {/home/aleksandar/Zotero/storage/I95EWHIC/S1566253520302918.html},
  journal = {Information Fusion},
  language = {en}
}

@article{xiaoTIMMETwitterIdeologydetection2020,
  title = {{{TIMME}}: {{Twitter Ideology}}-Detection via {{Multi}}-Task {{Multi}}-Relational {{Embedding}}},
  shorttitle = {{{TIMME}}},
  author = {Xiao, Zhiping and Song, Weiping and Xu, Haoyan and Ren, Zhicheng and Sun, Yizhou},
  year = {2020},
  month = jun,
  abstract = {We aim at solving the problem of predicting people's ideology, or political tendency. We estimate it by using Twitter data, and formalize it as a classification problem. Ideology-detection has long been a challenging yet important problem. Certain groups, such as the policy makers, rely on it to make wise decisions. Back in the old days when labor-intensive survey-studies were needed to collect public opinions, analyzing ordinary citizens' political tendencies was uneasy. The rise of social medias, such as Twitter, has enabled us to gather ordinary citizen's data easily. However, the incompleteness of the labels and the features in social network datasets is tricky, not to mention the enormous data size and the heterogeneousity. The data differ dramatically from many commonly-used datasets, thus brings unique challenges. In our work, first we built our own datasets from Twitter. Next, we proposed TIMME, a multi-task multi-relational embedding model, that works efficiently on sparsely-labeled heterogeneous real-world dataset. It could also handle the incompleteness of the input features. Experimental results showed that TIMME is overall better than the state-of-the-art models for ideology detection on Twitter. Our findings include: links can lead to good classification outcomes without text; conservative voice is under-represented on Twitter; follow is the most important relation to predict ideology; retweet and mention enhance a higher chance of like, etc. Last but not least, TIMME could be extended to other datasets and tasks in theory.},
  archivePrefix = {arXiv},
  eprint = {2006.01321},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Dropbox/Zotero/Xiao et al/Xiao et al_2020_TIMME.pdf;/home/aleksandar/Zotero/storage/INL3LYU2/2006.html},
  journal = {arXiv:2006.01321 [cs, stat]},
  primaryClass = {cs, stat}
}

@article{xieConsistencyMaximumLikelihood2020,
  title = {On the {{Consistency}} of {{Maximum Likelihood Estimators}} for {{Causal Network Identification}}},
  author = {Xie, Xiaotian and Katselis, Dimitrios and Beck, Carolyn L. and Srikant, R.},
  year = {2020},
  month = oct,
  abstract = {We consider the problem of identifying parameters of a particular class of Markov chains, called Bernoulli Autoregressive (BAR) processes. The structure of any BAR model is encoded by a directed graph. Incoming edges to a node in the graph indicate that the state of the node at a particular time instant is influenced by the states of the corresponding parental nodes in the previous time instant. The associated edge weights determine the corresponding level of influence from each parental node. In the simplest setup, the Bernoulli parameter of a particular node's state variable is a convex combination of the parental node states in the previous time instant and an additional Bernoulli noise random variable. This paper focuses on the problem of edge weight identification using Maximum Likelihood (ML) estimation and proves that the ML estimator is strongly consistent for two variants of the BAR model. We additionally derive closed-form estimators for the aforementioned two variants and prove their strong consistency.},
  archivePrefix = {arXiv},
  eprint = {2010.08870},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Xie et al/Xie et al_2020_On the Consistency of Maximum Likelihood Estimators for Causal Network.pdf;/home/aleksandar/Zotero/storage/I55WNN77/2010.html},
  journal = {arXiv:2010.08870 [cs, math, stat]},
  primaryClass = {cs, math, stat}
}

@article{xueEvolutionSocialPower2020,
  title = {Evolution of {{Social Power}} over {{Influence Networks Containing Antagonistic Interactions}}},
  author = {Xue, Dong and Hirche, Sandra and Cao, Ming},
  year = {2020},
  month = jun,
  issn = {0020-0255},
  doi = {10.1016/j.ins.2020.05.142},
  abstract = {Individual social power in the opinion formation process over social influence networks has been under intense scientific investigation. Most related works assume explicitly or implicitly that the interpersonal influence weights are always non-negative. In sharp comparison, we argue that such influence weights can be both positive and negative since there exist various contrasting relationships in real-world social networks. Hence, this article studies the evolution of opinion dynamics and social power on cooperative-competitive networks whose influence structure changes via a reflected appraisal mechanism along a sequence of issue discussions. Of particular focus is on identifying the pathways and effects of social power on shaping public opinions from a graph-theoretic perspective. Then, we propose a dynamic model for the reflected self-appraisal process, which enables us to discuss how the individual social power evolves over sequential issue discussions. By accommodating differential Lyapunov theory, we show the global exponential convergence of the self-appraisal model for almost all network topologies. Finally, we conclude that the self-appraisals and social powers are eventually dependent only on an interpersonal appraisal profile.},
  file = {/home/aleksandar/Documents/Zotero/Xue et al/Xue et al_2020_Evolution of Social Power over Influence Networks Containing Antagonistic.pdf},
  journal = {Information Sciences},
  keywords = {DONE},
  language = {en}
}

@article{xueNetworkTemporalityCan2020,
  title = {Network Temporality Can Promote and Suppress Information Spreading},
  author = {Xue, Xiaoyu and Pan, Liming and Zheng, Muhua and Wang, Wei},
  year = {2020},
  month = nov,
  volume = {30},
  pages = {113136},
  publisher = {{American Institute of Physics}},
  issn = {1054-1500},
  doi = {10.1063/5.0027758},
  file = {/home/aleksandar/Zotero/storage/W2PXSAAZ/5.html},
  journal = {Chaos: An Interdisciplinary Journal of Nonlinear Science},
  number = {11}
}

@article{xuIndividualDifferencesAttitude2020,
  title = {Individual {{Differences}} in {{Attitude Consistency Over Time}}: {{The Personal Attitude Stability Scale}}},
  shorttitle = {Individual {{Differences}} in {{Attitude Consistency Over Time}}},
  author = {Xu, Mengran and Bri{\~n}ol, Pablo and Gretton, Jeremy D. and Tormala, Zakary L. and Rucker, Derek D. and Petty, Richard E.},
  year = {2020},
  month = mar,
  pages = {0146167220908995},
  publisher = {{SAGE Publications Inc}},
  issn = {0146-1672},
  doi = {10.1177/0146167220908995},
  abstract = {This research finds evidence for reliable individual differences in people's perceived attitude stability that predict the actual stability of their attitudes over time. Study 1 examines the reliability and factor structure of an 11-item Personal Attitude Stability Scale (PASS). Study 2 establishes test\textendash retest reliability for the PASS over a 5-week period. Studies 3a and 3b demonstrate the convergent and discriminant validity of the PASS in relation to relevant existing individual differences. Studies 4 and 5 show that the PASS predicts attitude stability following a delay period across several distinct topics. Across multiple attitude objects, for people with high (vs. low) scores on the PASS, Time 1 attitudes were more predictive of their Time 2 attitudes, indicative of greater attitudinal consistency over time. The final study also demonstrates that the PASS predicts attitude stability above and beyond other related scales.},
  journal = {Personality and Social Psychology Bulletin},
  language = {en}
}

@article{xuOpinionMaximizationSocial2020,
  title = {Opinion {{Maximization}} in {{Social Trust Networks}}},
  author = {Xu, Pinghua and Hu, Wenbin and Wu, Jia and Liu, Weiwei},
  year = {2020},
  month = jun,
  abstract = {Social media sites are now becoming very important platforms for product promotion or marketing campaigns. Therefore, there is broad interest in determining ways to guide a site to react more positively to a product with a limited budget. However, the practical significance of the existing studies on this subject is limited for two reasons. First, most studies have investigated the issue in oversimplified networks in which several important network characteristics are ignored. Second, the opinions of individuals are modeled as bipartite states(e.g., support or not) in numerous studies, however, this setting is too strict for many real scenarios. In this study, we focus on social trust networks(STNs), which have the significant characteristics ignored in the previous studies. We generalized a famed continuous-valued opinion dynamics model for STNs, which is more consistent with real scenarios. We subsequently formalized two novel problems for solving the issue in STNs. Moreover, we developed two matrix-based methods for these two problems and experiments on real-world datasets to demonstrate the practical utility of our methods.},
  archivePrefix = {arXiv},
  eprint = {2006.10961},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Xu et al/Xu et al_2020_Opinion Maximization in Social Trust Networks.pdf;/home/aleksandar/Zotero/storage/DZW2ADEP/2006.html},
  journal = {arXiv:2006.10961 [cs]},
  keywords = {DONE},
  primaryClass = {cs}
}

@article{xuSensitivityAnalysisNetworkundefined/ed,
  title = {Sensitivity Analysis for Network Observations with Applications to Inferences of Social Influence Effects},
  author = {Xu, Ran and Frank, Kenneth A.},
  year = {undefined/ed},
  pages = {1--26},
  publisher = {{Cambridge University Press}},
  issn = {2050-1242, 2050-1250},
  doi = {10.1017/nws.2020.36},
  abstract = {The validity of network observations is sometimes of concern in empirical studies, since observed networks are prone to error and may not represent the population of interest. This lack of validity is not just a result of random measurement error, but often due to systematic bias that can lead to the misinterpretation of actors' preferences of network selections. These issues in network observations could bias the estimation of common network models (such as those pertaining to influence and selection) and lead to erroneous statistical inferences. In this study, we proposed a simulation-based sensitivity analysis method that can evaluate the robustness of inferences made in social network analysis to six forms of selection mechanisms that can cause biases in network observations\textemdash random, homophily, anti-homophily, transitivity, reciprocity, and preferential attachment. We then applied this sensitivity analysis to test the robustness of inferences for social influence effects, and we derived two sets of analytical solutions that can account for biases in network observations due to random, homophily, and anti-homophily selection.},
  file = {/home/aleksandar/Zotero/storage/Z6HR4IZH/C93F31E38EA80360A8A9725AEB1EBDEE.html},
  journal = {Network Science},
  language = {en}
}

@techreport{yanAsymmetricalPerceptionsPartisan2020,
  title = {Asymmetrical {{Perceptions}} of {{Partisan Political Bots}}},
  author = {Yan, Harry and Yang, Kai-Cheng and Menczer, Filippo and Shanahan, James},
  year = {2020},
  month = jun,
  institution = {{SocArXiv}},
  doi = {10.31235/osf.io/gf7jb},
  abstract = {Political bots are social media algorithms that impersonate political actors and interact with other users, aiming to influence public opinion. This study examines perceptions of bots with partisan personas by conducting an online experiment (N = 656) that examines the ability to differentiate bots from humans on Twitter. We explore how characteristics of the experiment participants and the profiles being evaluated bias recognition accuracy. Our analysis reveals asymmetrical partisan-motivated reasoning, in that conservative profiles appear to be more confusing and Republican participants perform less well in the recognition task. Moreover, Republican users are more likely to confuse conservative bots with humans, whereas Democratic users are more likely to confuse conservative human users with bots. We discuss implications for how partisan identities affect motivated reasoning and how political bots exacerbate political polarization.},
  file = {/home/aleksandar/Dropbox/Zotero/Yan et al/Yan et al_2020_Asymmetrical Perceptions of Partisan Political Bots.pdf},
  language = {en},
  type = {Preprint}
}

@article{yangComparativeAnalysisCommunity2016,
  title = {A Comparative Analysis of Community Detection Algorithms on Artificial Networks},
  author = {Yang, Zhao and Algesheimer, Ren{\'e} and Tessone, Claudio J.},
  year = {2016},
  month = aug,
  volume = {6},
  pages = {30750},
  journal = {Scientific Reports}
}

@article{yangEpidemicSpreadingEvolving2020,
  title = {Epidemic Spreading of Evolving Community Structure},
  author = {Yang, Jin-Xuan and Zhang, Yun},
  year = {2020},
  month = nov,
  volume = {140},
  pages = {110101},
  issn = {0960-0779},
  doi = {10.1016/j.chaos.2020.110101},
  abstract = {Many complex systems have common community structure characteristics. Studying the community structure can reveal the mechanism of complex systems, so as to better understand and control complex systems. In the present work, we regard a community as an approximate homogeneous network. We use a mean-field approach to obtain an epidemic spreading dynamic model of community structure evolution. The epidemic threshold is given. Randomness is one of the striking characteristics of real-world networks as they evolve. Based on the specific stochastic block model (SBM) to generate synthetic networks with community structure, we use nonnegative matrix theory to analyze the changes in the spread of epidemics during the growth, contraction, division or merger of communities. A reduction in the number of communities, a reduction in the size of communities or a division of communities is conductive to increasing epidemic threshold, and thus mitigating the spread of epidemics. Our conclusion can explain some phenomena. The numerical simulations of real-world and random networks support and enrich our conclusions.},
  file = {/home/aleksandar/Zotero/storage/IPYCMXHU/S0960077920304987.html},
  journal = {Chaos, Solitons \& Fractals},
  language = {en}
}

@article{yangGraphbasedLabelPropagation2019,
  title = {Graph-Based Label Propagation Algorithm for Community Detection},
  author = {Yang, Gui and Zheng, Wenping and Che, Chenhao and Wang, Wenjian},
  year = {2019},
  month = dec,
  issn = {1868-808X},
  doi = {10.1007/s13042-019-01042-0},
  abstract = {Community detection is one of the most important topics in complex network analysis. Among a variety of approaches for detecting communities, the label propagation algorithm (LPA) is the simplest and time-efficient approach. However, the original label propagation algorithm is not stable due to the randomness in its propagation process. In this paper, we propose a graph-based label propagation algorithm (GLPA) to detect communities incorporating the node similarity and connectivity information during the propagation of the labels. First, we define node similarity between adjacent nodes, and change each node's label to that of its most similar neighbor node. Based on the label propagation process, GLPA constructs a label propagation graph to get candidate communities. Then, GLPA calculates the connected components of the label propagation graph. Each connected component is treated as a candidate community in the next step. Second, GLPA constructs a weighted graph to obtain final communities, in which each connected component are treated as a super-node, and the number of edges lying between the corresponding components as the weight of edges. We compute the merging factor for each node in the weighted graph and merge super nodes with higher merging factor to its most similar node iteratively to reach the maximum complementary entropy. Compared with 8 other classical community detection algorithms on LFR artificial networks and 12 real world networks, the proposed algorithm GLPA shows preferable performance on stability, NMI, ARI, modularity.},
  file = {/home/aleksandar/Dropbox/Zotero/Yang et al/Yang et al_2019_Graph-based label propagation algorithm for community detection.pdf},
  journal = {International Journal of Machine Learning and Cybernetics},
  language = {en}
}

@article{yangHighDimensionalHyperbolic2020,
  title = {High {{Dimensional Hyperbolic Geometry}} of {{Complex Networks}}},
  author = {Yang, Weihua and Rideout, David},
  year = {2020},
  month = oct,
  volume = {8},
  pages = {1861},
  issn = {2227-7390},
  doi = {10.3390/math8111861},
  abstract = {High dimensional embeddings of graph data into hyperbolic space have recently been shown to have great value in encoding hierarchical structures, especially in the area of natural language processing, named entity recognition, and machine generation of ontologies. Given the striking success of these approaches, we extend the famous hyperbolic geometric random graph models of Krioukov et al. to arbitrary dimension, providing a detailed analysis of the degree distribution behavior of the model in an expanded portion of the parameter space, considering several regimes which have yet to be considered. Our analysis includes a study of the asymptotic correlations of degree in the network, revealing a non-trivial dependence on the dimension and power law exponent. These results pave the way to using hyperbolic geometric random graph models in high dimensional contexts, which may provide a new window into the internal states of network nodes, manifested only by their external interconnectivity.},
  file = {/home/aleksandar/Zotero/storage/GXX7C828/Yang and Rideout - 2020 - High Dimensional Hyperbolic Geometry of Complex Ne.pdf},
  journal = {Mathematics},
  language = {en},
  number = {11}
}

@article{yangMixedModelsApproach2006,
  title = {A {{Mixed Models Approach}} to the {{Age}}-{{Period}}-{{Cohort Analysis}} of {{Repeated Cross}}-{{Section Surveys}}, with an {{Application}} to {{Data}} on {{Trends}} in {{Verbal Test Scores}}},
  author = {Yang, Yang and Land, Kenneth C.},
  year = {2006},
  month = aug,
  volume = {36},
  pages = {75--97},
  issn = {0081-1750, 1467-9531},
  doi = {10.1111/j.1467-9531.2006.00175.x},
  journal = {Sociological Methodology},
  language = {en},
  number = {1}
}

@article{yangQuadraticOptimizationBased2020,
  title = {Quadratic {{Optimization}} Based {{Clique Expansion}} for {{Overlapping Community Detection}}},
  author = {Yang, Yanhao and Shi, Pan and Wang, Yuyi and He, Kun},
  year = {2020},
  month = nov,
  abstract = {Community detection is crucial for analyzing social and biological networks, and comprehensive approaches have been proposed in the last two decades. Nevertheless, finding all overlapping communities in large networks that could accurately approximate the ground-truth communities remains challenging. In this work, we present the QOCE (Quadratic Optimization based Clique Expansion), an overlapping community detection algorithm that could scale to large networks with hundreds of thousands of nodes and millions of edges. QOCE follows the popular seed set expansion strategy, regarding each high-quality maximal clique as the initial seed set and applying quadratic optimization for the expansion. We extensively evaluate our algorithm on 28 synthetic LFR networks and six real-world networks of various domains and scales, and compare QOCE with four state-of-the-art overlapping community detection algorithms. Empirical results demonstrate the competitive performance of the proposed approach in terms of detection accuracy, efficiency, and scalability.},
  archivePrefix = {arXiv},
  eprint = {2011.01640},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Yang et al/Yang et al_2020_Quadratic Optimization based Clique Expansion for Overlapping Community.pdf;/home/aleksandar/Zotero/storage/ARTY3QMT/2011.html},
  journal = {arXiv:2011.01640 [cs]},
  keywords = {Computer Science - Social and Information Networks},
  primaryClass = {cs}
}

@article{yangTuringPatternInduced2020,
  title = {Turing {{Pattern Induced}} by {{Cross}}-{{Diffusion}} in a {{Predator}}\textendash{{Prey Model}} with {{Pack Predation}}-{{Herd Behavior}}},
  author = {Yang, Jingen and Zhang, Tonghua and Yuan, Sanling},
  year = {2020},
  month = jun,
  volume = {30},
  pages = {2050103},
  publisher = {{World Scientific Publishing Co.}},
  issn = {0218-1274},
  doi = {10.1142/S0218127420501035},
  abstract = {In this paper, we propose a diffusive predator\textendash prey model with herd behavior in prey and pack predation behavior in predator under the homogeneous Neumann boundary condition. Due to the pack predation-herd behavior, the predator\textendash prey interaction occurs only at the outer edge of each group. First, we analyze the existence and local stability of the equilibria of temporal model using vector field analysis and characteristic equations. Second, we deduce the conditions under which Turing instability occurs with the help of linear stability analysis. Then, using standard multiple-scale analysis, we derive the amplitude equations for the excited modes. By numerical simulations, we find that the model exhibits complex pattern replication by varying the value of parameters aa{$<$}math display="inline" overflow="scroll" altimg="eq-00001.gif"{$><$}mi{$>$}a{$<$}/mi{$><$}/math{$>$} and d21d21{$<$}math display="inline" overflow="scroll" altimg="eq-00002.gif"{$><$}msub{$><$}mrow{$><$}mi{$>$}d{$<$}/mi{$><$}/mrow{$><$}mrow{$><$}mn{$>$}2{$<$}/mn{$><$}mn{$>$}1{$<$}/mn{$><$}/mrow{$><$}/msub{$><$}/math{$>$}, and study the relationships between the average density of both prey and predator and these two parameters.},
  file = {/home/aleksandar/Zotero/storage/JWNX8QD5/S0218127420501035.html},
  journal = {International Journal of Bifurcation and Chaos},
  number = {07}
}

@article{yangTwostepMethodEstimating2020,
  title = {A Two-Step Method for Estimating High-Dimensional {{Gaussian}} Graphical Models},
  author = {Yang, Yuehan and Zhu, Ji},
  year = {2020},
  month = may,
  issn = {1869-1862},
  doi = {10.1007/s11425-017-9438-5},
  abstract = {The problem of estimating high-dimensional Gaussian graphical models has gained much attention in recent years. Most existing methods can be considered as one-step approaches, being either regression-based or likelihood-based. In this paper, we propose a two-step method for estimating the high-dimensional Gaussian graphical model. Specifically, the first step serves as a screening step, in which many entries of the concentration matrix are identified as zeros and thus removed from further consideration. Then in the second step, we focus on the remaining entries of the concentration matrix and perform selection and estimation for nonzero entries of the concentration matrix. Since the dimension of the parameter space is effectively reduced by the screening step, the estimation accuracy of the estimated concentration matrix can be potentially improved. We show that the proposed method enjoys desirable asymptotic properties. Numerical comparisons of the proposed method with several existing methods indicate that the proposed method works well. We also apply the proposed method to a breast cancer microarray data set and obtain some biologically meaningful results.},
  journal = {Science China Mathematics},
  language = {en}
}

@article{yangTwoTalesComplex2020,
  title = {Two Tales of Complex System Analysis: {{MaxEnt}} and Agent-Based Modeling},
  shorttitle = {Two Tales of Complex System Analysis},
  author = {Yang, Jangho and Carro, Adri{\'a}n},
  year = {2020},
  month = jul,
  volume = {229},
  pages = {1623--1643},
  issn = {1951-6355, 1951-6401},
  doi = {10.1140/epjst/e2020-900137-x},
  abstract = {Over the recent four decades, agent-based modeling and maximum entropy modeling have provided some of the most notable contributions applying concepts from complexity science to a broad range of problems in economics. In this paper, we argue that these two seemingly unrelated approaches can actually complement each other, providing a powerful conceptual/empirical tool for the analysis of complex economic problems. The maximum entropy approach is particularly well suited for an analytically rigorous study of the qualitative properties of systems in quasi-equilibrium. Agent-based modeling, unconstrained by either equilibrium or analytical tractability considerations, can provide a richer picture of the system under study by allowing for a wider choice of behavioral assumptions. In order to demonstrate the complementarity of these approaches, we use here two simple economic models based on maximum entropy principles \textendash a quantal response social interaction model and a market feedback model \textendash, for which we develop agent-based equivalent models. On the one hand, this allows us to highlight the potential of maximum entropy models for guiding the development of well-grounded, firstapproximation agent-based models. On the other hand, we are also able to demonstrate the capabilities of agent-based models for tracking irreversible and out-of-equilibrium dynamics as well as for exploring the consequences of agent heterogeneity, thus fundamentally improving on the original maximum entropy model and potentially guiding its further extension.},
  file = {/home/aleksandar/Zotero/storage/2UKSA2W5/Yang and Carro - 2020 - Two tales of complex system analysis MaxEnt and a.pdf},
  journal = {The European Physical Journal Special Topics},
  language = {en},
  number = {9}
}

@article{yangWhyAreParties2020,
  title = {Why Are {{U}}.{{S}}. {{Parties So Polarized}}? {{A}} "{{Satisficing}}" {{Dynamical Model}}},
  shorttitle = {Why Are {{U}}.{{S}}. {{Parties So Polarized}}?},
  author = {Yang, Vicky Chuqiao and Abrams, Daniel M. and Kernell, Georgia and Motter, Adilson E.},
  year = {2020},
  month = jan,
  volume = {62},
  pages = {646--657},
  issn = {0036-1445, 1095-7200},
  doi = {10.1137/19M1254246},
  abstract = {Since the 1960s, Democrats and Republicans in U.S. Congress have taken increasingly polarized positions, while the public's policy positions have remained centrist and moderate. We explain this apparent contradiction by developing a dynamical model that predicts ideological positions of political parties. Our approach tackles the challenge of incorporating bounded rationality into mathematical models and integrates the empirical finding of satisficing decision making---voters settle for candidates who are "good enough" when deciding for whom to vote. We test the model using data from the U.S. Congress over the past 150 years, and find that our predictions are consistent with the two major political parties' historical trajectory. In particular, the model explains how polarization between the Democrats and Republicans since the 1960s could be a consequence of increasing ideological homogeneity within the parties.},
  archivePrefix = {arXiv},
  eprint = {2008.05661},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Yang et al/Yang et al_2020_Why are U.pdf;/home/aleksandar/Zotero/storage/KT8BRSB7/2008.html},
  journal = {SIAM Review},
  number = {3}
}

@techreport{yarkoniEnhancingAcceleratingSocial2019,
  title = {Enhancing and Accelerating Social Science via Automation: {{Challenges}} and Opportunities},
  shorttitle = {Enhancing and Accelerating Social Science via Automation},
  author = {Yarkoni, Tal and Eckles, Dean and Heathers, James and Levenstein, Margaret and Smaldino, Paul and Lane, Julia I.},
  year = {2019},
  month = jan,
  institution = {{SocArXiv}},
  doi = {10.31235/osf.io/vncwe},
  abstract = {Automation plays an increasingly important role in science, but the social sciences have been comparatively slow to take advantage of emerging technologies and methods. In this review, we argue that greater investment in automation would be one of the most effective and cost-effective ways to boost the reliability, validity, and utility of social science research. We identify five core areas ripe for potentially transformative investment, including (1) machine-readable standards, (2) data access platforms, (3) search and discoverability, (4) claim validation, and (5) insight generation. In each case, we review limitations associated with current practices, identify concrete opportunities for improvement via automation, and discuss near-term barriers to progress. We conclude with a discussion of practical and ethical considerations researchers will need to keep in mind when working to enhance and accelerate social science progress via automation.},
  type = {Preprint}
}

@inproceedings{yashkinaExpressingTrustTemporal2020,
  title = {Expressing {{Trust}} with {{Temporal Frequency}} of {{User Interaction}} in {{Online Communities}}},
  booktitle = {Advanced {{Information Networking}} and {{Applications}}},
  author = {Yashkina, Ekaterina and Pinigin, Arseny and Lee, JooYoung and Mazzara, Manuel and Adekotujo, Akinlolu Solomon and Zubair, Adam and Longo, Luca},
  editor = {Barolli, Leonard and Takizawa, Makoto and Xhafa, Fatos and Enokido, Tomoya},
  year = {2020},
  pages = {1133--1146},
  publisher = {{Springer International Publishing}},
  address = {{Cham}},
  doi = {10.1007/978-3-030-15032-7_95},
  abstract = {Reputation systems concern soft security dynamics in diverse areas. Trust dynamics in a reputation system should be stable and adaptable at the same time to serve the purpose. Many reputation mechanisms have been proposed and tested over time. However, the main drawback of reputation management is that users need to share private information to gain trust in a system such as phone numbers, reviews, and ratings. Recently, a novel model that tries to overcome this issue was presented: the Dynamic Interaction-based Reputation Model (DIBRM). This approach to trust considers only implicit information automatically deduced from the interactions of users within an online community. In this primary research study, the Reddit and MathOverflow online social communities have been selected for testing DIBRM. Results show how this novel approach to trust can mimic behaviors of the selected reputation systems, namely Reddit and MathOverflow, only with temporal information.},
  file = {/home/aleksandar/Zotero/storage/8GJMZC7L/Yashkina et al_2020_Expressing Trust with Temporal Frequency of User Interaction in Online.pdf},
  isbn = {978-3-030-15032-7},
  language = {en},
  series = {Advances in {{Intelligent Systems}} and {{Computing}}}
}

@misc{yeBipartiteConsensusHeterogeneous2020,
  title = {Bipartite {{Consensus}} of {{Heterogeneous Multiagent Systems}} with {{Diverse Input Delays}}},
  author = {Ye, Hongtao and Chen, Zhongqiu and Luo, Wenguang and Wen, Jiayan and Li, Kene},
  year = {2020},
  month = jul,
  volume = {2020},
  pages = {e4157980},
  publisher = {{Hindawi}},
  issn = {1076-2787},
  doi = {10.1155/2020/4157980},
  abstract = {This paper investigates the bipartite consensus problem of heterogeneous multiagent systems with diverse input delays. Based on the systems composed of first-order and second-order agents, the novel control protocols are designed. Using frequency-domain analysis and matrix theory, the corresponding upper bounds of the allowable delays are obtained under the undirected topology and directed topology, respectively. Finally, simulation examples are given to verify the theoretical analysis.},
  file = {/home/aleksandar/Dropbox/Zotero/Ye et al/Ye et al_2020_Bipartite Consensus of Heterogeneous Multiagent Systems with Diverse Input.pdf;/home/aleksandar/Zotero/storage/8NLLKATK/4157980.html},
  howpublished = {https://www.hindawi.com/journals/complexity/2020/4157980/},
  journal = {Complexity},
  language = {en},
  type = {Research {{Article}}}
}

@article{yeImpactIntraIntercluster2020,
  title = {Impact of Intra and Inter-Cluster Coupling Balance on the Performance of Nonlinear Networked Systems},
  author = {Ye, Jiachen and Ji, Peng and Waxman, David and Lin, Wei and Moreno, Yamir},
  year = {2020},
  month = jun,
  abstract = {The dynamical and structural aspects of cluster synchronization (CS) in complex systems have been intensively investigated in recent years. Here, we study CS of dynamical systems with intra and inter-cluster couplings. We propose new metrics that describe the performance of such systems and evaluate them as a function of the strength of the couplings within and between clusters. We obtain analytical results that indicate that spectral differences between the Laplacian matrices associated with the partition between intra and inter-couplings directly affect the proposed metrics of system performance. Our results show that the dynamics of the system might exhibit an optimal balance that optimizes its performance. Our work provides new insights into the way specific symmetry properties relate to collective behavior, and could lead to new forms to increase the controllability of complex systems and to optimize their stability.},
  archivePrefix = {arXiv},
  eprint = {2006.11357},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Dropbox/Zotero/Ye et al/Ye et al_2020_Impact of intra and inter-cluster coupling balance on the performance of.pdf;/home/aleksandar/Zotero/storage/5EDL3EFX/2006.html},
  journal = {arXiv:2006.11357 [physics]},
  primaryClass = {physics}
}

@article{yeStructureDynamicsKnowledge2016,
  title = {The Structure and Dynamics of Knowledge Network in Domain-Specific {{Q}}\&{{A}} Sites: A Case Study of Stack Overflow},
  shorttitle = {The Structure and Dynamics of Knowledge Network in Domain-Specific {{Q}}\&{{A}} Sites},
  author = {Ye, Deheng and Xing, Zhenchang and Kapre, Nachiket},
  year = {2016},
  doi = {10.1007/s10664-016-9430-z},
  abstract = {Programming-specific Q\&A sites (e.g., Stack Overflow) are being used extensively by software developers for knowledge sharing and acquisition. Due to the cross-reference of questions and answers (note that users also reference URLs external to the Q\&A site. In this paper, URL sharing refers to internal URLs within the Q\&A site, unless otherwise stated), knowledge is diffused in the Q\&A site, forming a large knowledge network. In Stack Overflow, why do developers share URLs? How is the community feedback to the knowledge being shared? What are the unique topological and semantic properties of the resulting knowledge network in Stack Overflow? Has this knowledge network become stable? If so, how does it reach to stability? Answering these questions can help the software engineering community better understand the knowledge diffusion process in programming-specific Q\&A sites like Stack Overflow, thereby enabling more effective knowledge sharing, knowledge use, and knowledge representation and search in the community. Previous work has focused on analyzing user activities in Q\&A sites or mining the textual content of these sites. In this article, we present a methodology to analyze URL sharing activities in Stack Overflow. We use open coding method to analyze why users share URLs in Stack Overflow, and develop a set of quantitative analysis methods to study the structural and dynamic properties of the emergent knowledge network in Stack Overflow. We also identify system designs, community norms, and social behavior theories that help explain our empirical findings. Through this study, we obtain an in-depth understanding of the knowledge diffusion process in Stack Overflow and expose the implications of URL sharing behavior for Q\&A site design, developers who use crowdsourced knowledge in Stack Overflow, and future research on knowledge representation and search.},
  file = {/home/aleksandar/Zotero/storage/2AVKU3KD/Ye et al_2016_The structure and dynamics of knowledge network in domain-specific Q&A sites.pdf},
  journal = {Empirical Software Engineering},
  keywords = {DONE}
}

@article{yinDemystifyLindleyParadox2020,
  title = {Demystify {{Lindley}}'s {{Paradox}} by {{Interpreting P}}-Value as {{Posterior Probability}}},
  author = {Yin, Guosheng and Shi, Haolun},
  year = {2020},
  month = feb,
  abstract = {In the hypothesis testing framework, p-value is often computed to determine rejection of the null hypothesis or not. On the other hand, Bayesian approaches typically compute the posterior probability of the null hypothesis to evaluate its plausibility. We revisit Lindley's paradox (Lindley, 1957) and demystify the conflicting results between Bayesian and frequentist hypothesis testing procedures by casting a two-sided hypothesis as a combination of two one-sided hypotheses along the opposite directions. This can naturally circumvent the ambiguities of assigning a point mass to the null and choices of using local or non-local prior distributions. As p-value solely depends on the observed data without incorporating any prior information, we consider non-informative prior distributions for fair comparisons with p-value. The equivalence of p-value and the Bayesian posterior probability of the null hypothesis can be established to reconcile Lindley's paradox. Extensive simulation studies are conducted with multivariate normal data and random effects models to examine the relationship between the p-value and posterior probability.},
  archivePrefix = {arXiv},
  eprint = {2002.10883},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Dropbox/Zotero/Yin_Shi/Yin_Shi_2020_Demystify Lindley's Paradox by Interpreting P-value as Posterior Probability.pdf;/home/aleksandar/Dropbox/Zotero/Yin_Shi/Yin_Shi_2020_Demystify Lindley's Paradox by Interpreting P-value as Posterior Probability2.pdf},
  journal = {arXiv:2002.10883 [stat]},
  language = {en},
  primaryClass = {stat}
}

@article{yoshidaInvestigationFormationLearning2020,
  title = {An Investigation into the Formation of Learning Groups on Social Media and Their Growth},
  author = {Yoshida, Masami and Xiong, Chun and Liu, Yunyun and Liu, Hongyan},
  year = {2020},
  month = jun,
  volume = {0},
  pages = {1--14},
  publisher = {{Routledge}},
  issn = {1049-4820},
  doi = {10.1080/10494820.2020.1780268},
  abstract = {This study aims to identify the formulation differences of groups in two online activities in a university course with different strategies by using social network analysis. Specifically, the connections within the members and identification of brokers were analyzed. Whilst the group enhancing a ``community of practice'' in WeChat by assigned posting showed larger evolution on the social graph, it lacked active involvement of students' friends and structural holes and their diameter were shown to increase towards the end. Another group enhancing a ``personal knowledge network'' in Twitter by their free posting showed no ties with any social actors, implying a cold-start and problems of sparsity in the social communication. From the discussion about brokers, we concluded similar future needs to develop their social skills to promote itinerant and liaison brokers to evolve learning groups socially.},
  annotation = {\_eprint: https://doi.org/10.1080/10494820.2020.1780268},
  file = {/home/aleksandar/Zotero/storage/YIIAKT6V/10494820.2020.html},
  journal = {Interactive Learning Environments},
  number = {0}
}

@article{yuanBoundaryconnectionDeletionStrategy2020,
  title = {Boundary-Connection Deletion Strategy Based Method for Community Detection in Complex Networks},
  author = {Yuan, Chao and Rong, Chuitian and Yao, Qingshuang},
  year = {2020},
  month = jun,
  issn = {1573-7497},
  doi = {10.1007/s10489-020-01762-9},
  abstract = {Community detection in complex networks is a difficult problem. Up to now, there is no very effective method to solve it. Recently, many community detection algorithms based on edge removal have been proposed. However, these edge removal methods often delete many key connections within communities and weaken (or destroy) the community structure of the network. This will make the network communities more difficult to be identified and reduce the accuracy and stability of the algorithm. This paper proposed a boundary connection deletion based community detection algorithm. Different from other algorithms, our algorithm focuses on identifying and removing the boundary connections between network modules. This can enhance the network community structure and get high quality network modules. With high performance, our algorithm can detect the optimal and hierarchical community structure in weighted networks simultaneously. In order to verify the effectiveness of our algorithm, the stability and robustness of our algorithm were firstly analyzed. Then a series of experiments had been done on the real-world and synthetic networks. The real-world networks include Zachary's karate club network, dolphin social network, American college foot-ball network, PolBooks network, Les Mis\'erables character network, and the coauthorship network of scientists; The synthetic networks include GN benchmark and LFR benckmark. Two indices NMI and ModularityQ were used to compare our algorithm with the recently proposed algorithms, including meta-LPAm+, Srinivas and Rajendran's model, IDPM, CFCDs, EDCD, CNM, and CDASS. Experimental results show that our algorithm has better performance than these algorithms.},
  file = {/home/aleksandar/Dropbox/Zotero/Yuan et al/Yuan et al_2020_Boundary-connection deletion strategy based method for community detection in.pdf},
  journal = {Applied Intelligence},
  language = {en}
}

@techreport{yucelBeingKnowSocial2020,
  title = {Being in the Know: {{Social}} Network Analysis of Gossip and Friendship on College Campuses},
  shorttitle = {Being in the Know},
  author = {Yucel, Meltem and Sjobeck, Gustav and Glass, Rebecca and Rottman, Josh},
  year = {2020},
  month = jun,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/q8m7u},
  abstract = {Gossip (evaluative talk about others) is ubiquitous. Gossip allows important rules to be clarified and reinforced, and it allows individuals to keep track of their social networks while strengthening their bonds to the group (Fine, 1977; Foster, 2004). To measure how gossip relates to friendship, participants from a Men's and Women's collegiate crew team noted their friendship connections and their tendencies to gossip about each of their teammates. Using social network analysis, we found that the crew members' friend group connectedness significantly correlated with their positive and negative gossip network involvement. Higher connectedness amongst friends was associated with less involvement in spreading negative gossip and/or being a target of negative gossip. More central connectedness to the friend group was associated with more involvement in spreading positive gossip and/or being a target of positive gossip. These results suggest that the spread of both positive and negative gossip may influence and be influenced by friendship connections in a social network.},
  file = {/home/aleksandar/Dropbox/Zotero/Yucel et al/Yucel et al_2020_Being in the know.pdf},
  language = {en},
  type = {Preprint}
}

@article{yuNovelEvolutionaryClustering,
  title = {A Novel Evolutionary Clustering via the First-Order Varying Information for Dynamic Networks},
  author = {Yu, Wei and Wang, Wenjun and Chen, Xue and Pan, Lin and Jiao, Pengfei},
  doi = {S0378437119300196},
  abstract = {{$<$}p{$>$}Publication date: