text:
ality indices have been proposed to find prominent vertices, such as the eigenvector centrality and PageRank algorithm. PageRank-based algorithms are the most popular approaches to handle this task, since they are more suitable for directed networks, which are common situations in social media. However, the realistic problem in social networks is that the process to find true important persons is very complicated, since we should consider both how the influence of a vertex affects others and how many others follow a given vertex. However, past PageRank-based algorithms can only reflect the importance on the one side and ignore the influence on the other side. In addition, past algorithms only view the transition from one status to the next status as a linear process without considering more complicated situations. In this paper, we develop a novel centrality to find key persons within a social network by a proposed synthesized index which accounts for both the inflow and outflow matrices of a vertex. Besides, we propose different transition functions to represent the relationship from status to status. The empirical studies compare the proposed algorithms with the conventional algorithms and show the differences and flexibility of the proposed algorithm.},
  file = {/home/aleksandar/Zotero/storage/2TKMHIFW/Chen and Huang - 2020 - A Novel Centrality for Finding Key Persons in a So.pdf},
  language = {en}
}

@article{chenOperationalIdeologyParty2016,
  title = {Operational Ideology and Party Identification: {{A}} Dynamic Model of Individual-Level Change in Partisan and Ideological Predispositions},
  shorttitle = {Operational Ideology and Party Identification},
  author = {Chen, Philip G. and Goren, Paul N.},
  year = {2016},
  volume = {69},
  pages = {703--715},
  publisher = {{SAGE Publications Sage CA: Los Angeles, CA}},
  journal = {Political Research Quarterly},
  number = {4}
}

@misc{chenOpinionDynamicsModel2019,
  title = {Opinion {{Dynamics Model Based}} on {{Cognitive Styles}}: {{Field}}-{{Dependence}} and {{Field}}-{{Independence}}},
  shorttitle = {Opinion {{Dynamics Model Based}} on {{Cognitive Styles}}},
  author = {Chen, Xi and Zhao, Shen and Li, Wei},
  year = {2019},
  month = feb,
  volume = {2019},
  pages = {e2864124},
  publisher = {{Hindawi}},
  issn = {1076-2787},
  doi = {10.1155/2019/2864124},
  abstract = {Two distinct cognitive styles exist from the perspective of cognition: field-dependence and field-independence. In most public opinion dynamics models, people only consider that individuals update their opinions through interactions with other individuals. This represents the field-dependent cognitive style of the individual. The field-independent cognitive style is ignored in such cases. We consider both cognitive styles in public opinion dynamics and propose a public opinion evolution model based on cognitive styles (CS model). The opinions of neighbors and experiences of the individual represent field-dependent cognition and field-independent cognition, respectively, and the individual combines both cognitive styles to update his/her own opinion. In the proposed model, the experience parameter is designed to represent the weight of the current opinion in terms of the individual's experiences and the cognitive parameter is proposed to represent the tendencies of his/her cognitive styles. We experimentally verify that the CS and Hegselmann\textendash Krause (HK) models are similar in terms of public opinion evolution trends; with an increase in radius of confidence, the steady state of a social system shifts from divergence to polarization and eventually reaches consensus. Considering that individuals from different cultures have different degrees of inclination for the two styles, we present experiments focusing on cognitive parameter and experience parameter and analyze the evolutionary trends of opinion dynamics in different styles. We find that when an individual has a greater tendency toward the field-independent cognitive style under the influence of culture, the time required for a social system to reach a steady state will increase; the system will have greater difficultly in reaching consensus, mirroring the evolutionary trends in public opinion in the context of eastern and western cultures. The CS model constitutes an opinion dynamics model that is more consistent with the real world and may also serve as a basis for future cross-cultural research.},
  file = {/home/aleksandar/Dropbox/Zotero/Chen et al/Chen et al_2019_Opinion Dynamics Model Based on Cognitive Styles.pdf;/home/aleksandar/Zotero/storage/TT94KJTF/2864124.html},
  howpublished = {https://www.hindawi.com/journals/complexity/2019/2864124/},
  journal = {Complexity},
  language = {en},
  type = {Research {{Article}}}
}

@article{chenRelationshipBeliefProsocial2020,
  title = {The Relationship between Belief and Prosocial Behavior},
  author = {Chen, Outong and Guan, Fang and Hu, Yousong and Wu, Tingting and Liu, Lihong and Sheng, Jing and Chen, Jun},
  year = {2020},
  month = jul,
  issn = {1936-4733},
  doi = {10.1007/s12144-020-00943-6},
  abstract = {Recent studies have revealed a relationship between different beliefs and prosocial tendencies. Using empirical methods, we investigated whether belief, as a system, can promote prosocial tendencies. We inferred that inspiration plays a moderating role in developing prosocial tendencies. In study 1, we adopted measures from previous studies to probe the potential correlation between belief and prosocial tendencies. In study 2, participants were randomly assigned to belief, neutral, or disbelief conditions to accept a priming task and then completed a prosocial tendencies measure. We further designed study 3 to replicate the results. The results of study 1 showed a strong positive correlation between belief and prosocial tendencies. The results of study 2 demonstrated that belief promotes prosocial tendencies, while disbelief priming yields no effect. And these results were confirmed by study 3. In conclusion, belief was positively correlated with prosocial tendencies. A further study found that belief significantly predicted prosocial tendencies only under the low-inspiration condition, and the moderating role of inspiration was also verified. These results provided a feasible and novel way to reveal the mechanism of the relationship between belief and prosocial tendencies.},
  journal = {Current Psychology},
  language = {en}
}

@article{chenTopicsensitiveTrustEvaluation2020,
  title = {A Topic-Sensitive Trust Evaluation Approach for Users in Online Communities},
  author = {Chen, Xu and Yuan, Yuyu and Orgun, Mehmet Ali and Lu, Lilei},
  year = {2020},
  month = apr,
  volume = {194},
  pages = {105546},
  issn = {0950-7051},
  doi = {10.1016/j.knosys.2020.105546},
  abstract = {In order to facilitate human decision making, trust evaluation has received widespread attention in many fields, especially for online services. Most of the existing methods consider trust in a person as a value which does not vary across different scenarios without any attention to the distinction of domains or communities where trust is derived. However, the notion of context is a significant and indispensable factor for trust evaluation in practice. Due to the lack of the consideration of context, traditional methods cannot resolve the issue that arises when a highly trustworthy person in one domain is likely to dominate the results of trust assessment in others where the person is in fact less authoritative. To solve this problem, in this paper, we develop a general approach to accomplish topic-sensitive trust evaluation by considering the context of trust. We first propose a general framework which presents the well-organized architecture of topic-sensitive trust evaluation in online communities. Then, a user-topic model is proposed to automatically extract topic data from user-generated content based on the Labeled Latent Dirichlet Allocation (LLDA) model. To compare the topic differences between users, we design a topic coverage function for revealing their trust relationships in diverse topics. Moreover, we employ two traditional methods and extend them to accomplish trust prediction for people with multiple domain knowledge. Experiments based on a real-world dataset show that extended topic-sensitive approaches are more adaptive and accurate than those topic-free trust evaluation approaches, especially when the trust application scenario features multiple topics.},
  journal = {Knowledge-Based Systems},
  language = {en}
}

@article{cheriyanNormViolationOnline2020,
  title = {Norm Violation in Online Communities - {{A}} Study of {{Stack Overflow}} Comments},
  author = {Cheriyan, Jithin and Savarimuthu, B. and Cranefield, Stephen},
  year = {2020},
  abstract = {Norms are behavioral expectations in communities. Online communities are also expected to abide by the rules and regulations that are expressed in the code of conduct of a system. Even though community authorities continuously prompt their users to follow the regulations, it is observed that hate speech and abusive language usage are on the rise. In this paper, we quantify and analyze the patterns of violations of normative behaviour among the users of Stack Overflow (SO) - a well-known technical question-answer site for professionals and enthusiast programmers, while posting a comment. Even though the site has been dedicated to technical problem solving and debugging, hate speech as well as posting offensive comments make the community "toxic". By identifying and minimising various patterns of norm violations in different SO communities, the community would become less toxic and thereby the community can engage more effectively in its goal of knowledge sharing. Moreover, through automatic detection of such comments, the authors can be warned by the moderators, so that it is less likely to be repeated, thereby the reputation of the site and community can be improved. Based on the comments extracted from two different data sources on SO, this work first presents a taxonomy of norms that are violated. Second, it demonstrates the sanctions for certain norm violations. Third, it proposes a recommendation system that can be used to warn users that they are about to violate a norm. This can help achieve norm adherence in online communities.},
  file = {/home/aleksandar/Documents/Zotero/Cheriyan et al/Cheriyan et al_2020_Norm violation in online communities - A study of Stack Overflow comments.pdf},
  journal = {ArXiv}
}

@book{chhibberFormationNationalParty2004,
  title = {The Formation of National Party Systems: {{Federalism}} and Party Competition in {{Canada}}, {{Great Britain}}, {{India}}, and the {{United States}}},
  shorttitle = {The Formation of National Party Systems},
  author = {Chhibber, Pradeep K. and Kollman, Ken},
  year = {2004},
  publisher = {{Princeton University Press}},
  address = {{Princeton}},
  annotation = {OCLC: ocm53360632},
  isbn = {978-0-691-11931-1 978-0-691-11932-8},
  lccn = {JF2011 .C53 2004}
}

@article{chianeseExperimentsDeepLearning,
  title = {Some Experiments on {{Deep Learning}} for {{Fake News Detection}}},
  author = {Chianese, Angelo and Masciari, Elio and Moscato, Vincenzo and Picariello, Antonio and Sperli, Giancarlo},
  pages = {8},
  abstract = {The uncontrolled growth of fake news creation and dissemination we observed in recent years causes continuous threats to democracy, justice, and public trust. This problem has significantly driven the effort of both academia and industries for developing more accurate fake news detection strategies. Early detection of fake news is crucial, however the availability of information about news propagation is limited. Moreover, it has been shown that people tend to believe more fake news due to their features [13]. In this paper, we present our framework for fake news detection and we discuss in detail a solution based on deep learning methodologies we implemented by leveraging Google Bert features. Our experiments conducted on two well-known and widely used real-world datasets suggest that our method can outperform the stateof-the-art approaches and allows fake news accurate detection, even in the case of limited content information.},
  file = {/home/aleksandar/Dropbox/Zotero/Chianese et al/Chianese et al_Some experiments on Deep Learning for Fake News Detection.pdf},
  language = {en}
}

@article{chmielVeritableZoologySuccessive2020,
  title = {A Veritable Zoology of Successive Phase Transitions in the Asymmetric \$q\$-Voter Model on Multiplex Networks},
  author = {Chmiel, Anna and Sienkiewicz, Julian and Fronczak, Agata and Fronczak, Piotr},
  year = {2020},
  month = jul,
  abstract = {We analyze a nonlinear \$q\$-voter model with stochastic noise, interpreted in the social context as independence, on a duplex network. The size of the lobby \$q\$ (i.e., the pressure group) is a crucial parameter that changes the behavior of the system. The \$q\$-voter model has been applied on multiplex networks in a previous work [Phys. Rev E. 92. 052812. (2015)], and it has been shown that the character of the phase transition depends on the number of levels in the multiplex network as well as the value of \$q\$. Here we study phase transition character in the case when on each level of the network the lobby size is different, resulting in two parameters \$q\_1\$ and \$q\_2\$. We find evidence of successive phase transitions when a continuous phase transition is followed by a discontinuous one or two consecutive discontinuous phases appear, depending on the parameter. When analyzing this system, we even encounter mixed-order (or hybrid) phase transition. We perform simulations and obtain supporting analytical solutions on a simple multiplex case - a duplex clique, which consists of two fully overlapped complete graphs (cliques).},
  archivePrefix = {arXiv},
  eprint = {2007.12168},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Dropbox/Zotero/Chmiel et al/Chmiel et al_2020_A veritable zoology of successive phase transitions in the asymmetric $q$-voter.pdf;/home/aleksandar/Zotero/storage/934WFPAT/2007.html},
  journal = {arXiv:2007.12168 [cond-mat, physics:physics]},
  primaryClass = {cond-mat, physics:physics}
}

@inproceedings{choiTenSocialDimensions2020,
  title = {Ten {{Social Dimensions}} of {{Conversations}} and {{Relationships}}},
  booktitle = {Proceedings of {{The Web Conference}} 2020},
  author = {Choi, Minje and Aiello, Luca Maria and Varga, Kriszti{\'a}n Zsolt and Quercia, Daniele},
  year = {2020},
  month = apr,
  pages = {1514--1525},
  publisher = {{Association for Computing Machinery}},
  address = {{Taipei, Taiwan}},
  doi = {10.1145/3366423.3380224},
  abstract = {Decades of social science research identified ten fundamental dimensions that provide the conceptual building blocks to describe the nature of human relationships. Yet, it is not clear to what extent these concepts are expressed in everyday language and what role they have in shaping observable dynamics of social interactions. After annotating conversational text through crowdsourcing, we trained NLP tools to detect the presence of these types of interaction from conversations, and applied them to 160M messages written by geo-referenced Reddit users, 290k emails from the Enron corpus and 300k lines of dialogue from movie scripts. We show that social dimensions can be predicted purely from conversations with an AUC up to 0.98, and that the combination of the predicted dimensions suggests both the types of relationships people entertain (conflict vs. support) and the types of real-world communities (wealthy vs. deprived) they shape.},
  file = {/home/aleksandar/Dropbox/Zotero/Choi et al/Choi et al_2020_Ten Social Dimensions of Conversations and Relationships.pdf},
  isbn = {978-1-4503-7023-3},
  series = {{{WWW}} '20}
}

@article{choongOptimizingVariationalGraph2020,
  title = {Optimizing {{Variational Graph Autoencoder}} for {{Community Detection}} with {{Dual Optimization}}},
  author = {Choong, Jun Jin and Liu, Xin and Murata, Tsuyoshi},
  year = {2020},
  month = feb,
  volume = {22},
  pages = {197},
  issn = {1099-4300},
  doi = {10.3390/e22020197},
  abstract = {Variational Graph Autoencoder (VGAE) has recently gained traction for learning representations on graphs. Its inception has allowed models to achieve state-of-the-art performance for challenging tasks such as link prediction, rating prediction, and node clustering. However, a fundamental flaw exists in Variational Autoencoder (VAE)-based approaches. Specifically, merely minimizing the loss of VAE increases the deviation from its primary objective. Focusing on Variational Graph Autoencoder for Community Detection (VGAECD) we found that optimizing the loss using the stochastic gradient descent often leads to sub-optimal community structure especially when initialized poorly. We address this shortcoming by introducing a dual optimization procedure. This procedure aims to guide the optimization process and encourage learning of the primary objective. Additionally, we linearize the encoder to reduce the number of learning parameters. The outcome is a robust algorithm that outperforms its predecessor.},
  file = {/home/aleksandar/Dropbox/Zotero/Choong et al/Choong et al_2020_Optimizing Variational Graph Autoencoder for Community Detection with Dual.pdf},
  journal = {Entropy},
  language = {en},
  number = {2}
}

@article{chouzenouxGraphEMEMAlgorithm,
  ids = {chouzenouxGraphEMEMAlgorithma,chouzenouxGraphEMEMAlgorithmb},
  title = {{{GraphEM}}: {{EM}} Algorithm for Blind {{Kalman}} Filtering under Graphical Sparsity Constraints. ({{arXiv}}:2001.03195v1 [Math.{{OC}}])},
  author = {Chouzenoux, {\'E}milie and Elvira, V{\'i}ctor},
  doi = {arXiv:2001.03195v1},
  abstract = {Modeling and inference with multivariate sequences is central in a number of signal processing applications such as acoustics, social network analysis, biomedical, and finance, to name a few. The linear-Gaussian state-space model is a common way to describe a time series through the evolution of a hidden state, with the advantage of presenting a simple inference procedure due to the celebrated Kalman filter. A fundamental question when analyzing multivariate sequences is the search for relationships between their entries (or the modeled hidden states), especially when the inherent structure is a non-fully connected graph. In such context, graphical modeling combined with parsimony constraints allows to limit the proliferation of parameters and enables a compact data representation which is easier to interpret by the experts. In this work, we propose a novel expectation-minimization algorithm for estimating the linear matrix operator in the state equation of a linear-Gaussian state-space model. Lasso regularization is included in the M-step, that we solved using a proximal splitting Douglas-Rachford algorithm. Numerical experiments illustrate the benefits of the proposed model and inference technique, named GraphEM, over competitors relying on Granger causality.},
  journal = {arXiv Optimization and Control}
}

@article{chowellScalingLawsMovement2003,
  title = {Scaling Laws for the Movement of People between Locations in a Large City},
  author = {Chowell, G. and Hyman, J. M. and Eubank, S. and {Castillo-Chavez}, C.},
  year = {2003},
  month = dec,
  volume = {68},
  issn = {1063-651X, 1095-3787},
  doi = {10.1103/PhysRevE.68.066102},
  journal = {Physical Review E},
  language = {en},
  number = {6}
}

@techreport{christensenEstimatingFactorsPsychometric2020,
  title = {Estimating {{Factors}} with {{Psychometric Networks}}: {{A Monte Carlo Simulation Comparing Community Detection Algorithms}}},
  shorttitle = {Estimating {{Factors}} with {{Psychometric Networks}}},
  author = {Christensen, Alexander P. and Golino, Hudson},
  year = {2020},
  month = aug,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/hz89e},
  abstract = {Estimating the number of factors in multivariate data is at the crux of psychological measurement. Factor analysis has a long tradition in the field but it's been challenged recently by exploratory graph analysis (EGA), an approach based on network psychometrics. EGA first estimates a regularized partial correlation network using the graphical least absolute shrinkage and selection operator (GLASSO), and then applies the Walktrap community detection algorithm, which identifies communities (or factors) in the network. Simulation studies have demonstrated that EGA has comparable or better accuracy than contemporary state-of-the-art factor analytic methods (e.g., parallel analysis), while providing some additional advantages such as not requiring rotations and deterministic allocation of items into factors. Despite EGA's effectiveness, there has yet to be an investigation into whether other community detection algorithms could achieve equivalent or better perfomance. In the present study, we performed a Monte Carlo simulation using the GLASSO and two variants of a non-regularized partial correlation network estimation method and several community detection algorithms in the open-source igraph package in R. The purpose of the present study was to critically examine whether the network estimation and community detection components of EGA are optimal for estimating factors in psychological data as well as to provide a systematic investigation into how different community detection algorithms perform ``out-of-the-box.'' The results indicate that the Fast-greedy, Louvain, and Walktrap algorithms paired with the GLASSO method were consistently among the most accurate and least biased across conditions.},
  file = {/home/aleksandar/Zotero/storage/RLAVKSDX/Christensen and Golino - 2020 - Estimating Factors with Psychometric Networks A M.pdf},
  keywords = {TODO},
  language = {en},
  type = {Preprint}
}

@article{christensenEstimatingStabilityNumber,
  title = {Estimating the Stability of the Number of Factors via {{Bootstrap Exploratory Graph Analysis}}: {{A}} Tutorial},
  shorttitle = {Estimating the Stability of the Number of Factors via {{Bootstrap Exploratory Graph Analysis}}},
  author = {Christensen, Alexander P. and Golino, Hudson},
  doi = {10.31234/osf.io/9deay},
  abstract = {Exploratory graph analysis (EGA) is a recent addition to the growing field of network psychometrics. EGA has emerged as a popular approach for estimating the dimensonality of data in networks. The appeal of EGA is the visualization of the relations between variables and the deterministic allocation of variables into dimensions. Notably, networks tend to be sample-specific, making reproducibility and generalizability a key issue in network psychometrics. To resolve this issue, we've developed a novel bootstrap approach called, Bootstrap Exploratory Graph Analysis (bootEGA). bootEGA provides researchers with dimension and item stability statistics as well as item analyses that are akin to exploratory factor analysis loadings. We provide descriptions of bootEGA's functions accompanied by a step-by-step R tutorial for how to apply and interpret bootEGA's results. This tutorial is applied to real-world data to demonstrate its effectiveness at identifying problematic dimensions and items. In short, our results show that bootEGA is a robust approach for identifying the stability and robustness of dimensionality in data.}
}

@techreport{christensenEstimatingStabilityNumber2019,
  title = {Estimating the Stability of the Number of Factors via {{Bootstrap Exploratory Graph Analysis}}: {{A}} Tutorial},
  shorttitle = {Estimating the Stability of the Number of Factors via {{Bootstrap Exploratory Graph Analysis}}},
  author = {Christensen, Alexander P. and Golino, Hudson},
  year = {2019},
  month = feb,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/9deay},
  abstract = {Exploratory graph analysis (EGA) is a recent addition to the growing field of network psychometrics. EGA has emerged as a popular approach for estimating the dimensonality of data in networks. The appeal of EGA is the visualization of the relations between variables and the deterministic allocation of variables into dimensions. Notably, networks tend to be sample-specific, making reproducibility and generalizability a key issue in network psychometrics. To resolve this issue, we've developed a novel bootstrap approach called, Bootstrap Exploratory Graph Analysis (bootEGA). bootEGA provides researchers with dimension and item stability statistics as well as item analyses that are akin to exploratory factor analysis loadings. We provide descriptions of bootEGA's functions accompanied by a step-by-step R tutorial for how to apply and interpret bootEGA's results. This tutorial is applied to real-world data to demonstrate its effectiveness at identifying problematic dimensions and items. In short, our results show that bootEGA is a robust approach for identifying the stability and robustness of dimensionality in data.},
  file = {/home/aleksandar/Dropbox/Zotero/Christensen_Golino/Christensen_Golino_2019_Estimating the stability of the number of factors via Bootstrap Exploratory.pdf},
  type = {Preprint}
}

@techreport{christensenEstimatingStabilityNumber2019a,
  title = {Estimating the Stability of the Number of Factors via {{Bootstrap Exploratory Graph Analysis}}: {{A}} Tutorial},
  shorttitle = {Estimating the Stability of the Number of Factors via {{Bootstrap Exploratory Graph Analysis}}},
  author = {Christensen, Alexander P. and Golino, Hudson},
  year = {2019},
  month = feb,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/9deay},
  abstract = {Exploratory graph analysis (EGA) is a recent addition to the growing field of network psychometrics. EGA has emerged as a popular approach for estimating the dimensonality of data in networks. The appeal of EGA is the visualization of the relations between variables and the deterministic allocation of variables into dimensions. Notably, networks tend to be sample-specific, making reproducibility and generalizability a key issue in network psychometrics. To resolve this issue, we've developed a novel bootstrap approach called, Bootstrap Exploratory Graph Analysis (bootEGA). bootEGA provides researchers with dimension and item stability statistics as well as item analyses that are akin to exploratory factor analysis loadings. We provide descriptions of bootEGA's functions accompanied by a step-by-step R tutorial for how to apply and interpret bootEGA's results. This tutorial is applied to real-world data to demonstrate its effectiveness at identifying problematic dimensions and items. In short, our results show that bootEGA is a robust approach for identifying the stability and robustness of dimensionality in data.},
  keywords = {TODO},
  type = {Preprint}
}

@article{christensenNetworkStructureWisconsin2018,
  title = {Network Structure of the {{Wisconsin Schizotypy Scales}}\textendash{{Short Forms}}: {{Examining}} Psychometric Network Filtering Approaches},
  shorttitle = {Network Structure of the {{Wisconsin Schizotypy Scales}}\textendash{{Short Forms}}},
  author = {Christensen, Alexander P. and Kenett, Yoed N. and Aste, Tomaso and Silvia, Paul J. and Kwapil, Thomas R.},
  year = {2018},
  month = dec,
  volume = {50},
  pages = {2531--2550},
  issn = {1554-3528},
  doi = {10.3758/s13428-018-1032-9},
  abstract = {Schizotypy is a multidimensional construct that provides a useful framework for understanding the etiology, development, and risk for schizophrenia-spectrum disorders. Past research has applied traditional methods, such as factor analysis, to uncovering common dimensions of schizotypy. In the present study, we aimed to advance the construct of schizotypy, measured by the Wisconsin Schizotypy Scales\textendash Short Forms (WSS-SF), beyond this general scope by applying two different psychometric network filtering approaches\textemdash the state-of-the-art approach (lasso), which has been employed in previous studies, and an alternative approach (information-filtering networks; IFNs). First, we applied both filtering approaches to two large, independent samples of WSS-SF data (ns = 5,831 and 2,171) and assessed each approach's representation of the WSS-SF's schizotypy construct. Both filtering approaches produced results similar to those from traditional methods, with the IFN approach producing results more consistent with previous theoretical interpretations of schizotypy. Then we evaluated how well both filtering approaches reproduced the global and local network characteristics of the two samples. We found that the IFN approach produced more consistent results for both global and local network characteristics. Finally, we sought to evaluate the predictability of the network centrality measures for each filtering approach, by determining the core, intermediate, and peripheral items on the WSS-SF and using them to predict interview reports of schizophrenia-spectrum symptoms. We found some similarities and differences in their effectiveness, with the IFN approach's network structure providing better overall predictive distinctions. We discuss the implications of our findings for schizotypy and for psychometric network analysis more generally.},
  file = {/home/aleksandar/Zotero/storage/4XQJMZIL/Christensen et al_2018_Network structure of the Wisconsin Schizotypy Scales–Short Forms.pdf},
  journal = {Behavior Research Methods},
  language = {en},
  number = {6}
}

@techreport{christensenPsychometricNetworkPerspective2019,
  title = {A {{Psychometric Network Perspective}} on the {{Measurement}} and {{Assessment}} of {{Personality Traits}}},
  author = {Christensen, Alexander P. and Golino, Hudson and Silvia, Paul},
  year = {2019},
  month = mar,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/ktejp},
  abstract = {Nearly all personality measurement and assessment has been shaped by traditional psychometric methods such as classical test theory. Despite advances in personality theory and psychometrics, most personality assessment instruments were developed using traditional psychometric concepts and measures. In recent years, a new field of network psychometrics has emerged, which embraces the inherent complexity of personality and promises to change the way researchers think about psychological constructs. With this new tool comes the opportunity to refine and advance measurement and assessment practices, particularly in personality research. In this paper, we briefly review modern personality theories and discuss how psychometric network models can inform their conjectures. After, we review trends in personality measurement and show how psychometric networks complement these movements. Then, we put forward a psychometric network framework for developing personality assessment instruments. Within this framework, we connect network measures to traditional psychometric measures and provide guidelines for how psychometric networks can be used to develop personality scales. We argue that psychometric networks allow better control over the construct being measured, providing greater specificity in the assessment of psychological attributes. Several future directions emphasizing the development and improvement of this framework are provided.},
  type = {Preprint}
}

@article{christensenPsychometricNetworkPerspective2020,
  title = {A {{Psychometric Network Perspective}} on the {{Validity}} and {{Validation}} of {{Personality Trait Questionnaires}}},
  author = {Christensen, Alexander P. and Golino, Hudson and Silvia, Paul J.},
  year = {2020},
  issn = {1099-0984},
  doi = {10.1002/per.2265},
  abstract = {This article reviews the causal implications of latent variable and psychometric network models for the validation of personality trait questionnaires. These models imply different data generating mechanisms that have important consequences for the validity and validation of questionnaires. From this review, we formalize a framework for assessing the evidence for the validity of questionnaires from the psychometric network perspective. We focus specifically on the structural phase of validation, where items are assessed for redundancy, dimensionality, and internal structure. In this discussion, we underline the importance of identifying unique personality components (i.e. an item or set of items that share a unique common cause) and representing the breadth of each trait's domain in personality networks. After, we argue that psychometric network models have measures that are statistically equivalent to factor models but we suggest that their substantive interpretations differ. Finally, we provide a novel measure of structural consistency, which provides complementary information to internal consistency measures. We close with future directions for how external validation can be executed using psychometric network models. \textcopyright{} 2020 European Association of Personality Psychology},
  annotation = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/per.2265},
  copyright = {\textcopyright{} 2020 European Association of Personality Psychology},
  file = {/home/aleksandar/Zotero/storage/CWYHRV88/Christensen et al_A Psychometric Network Perspective on the Validity and Validation of.pdf;/home/aleksandar/Zotero/storage/XWXFE8A6/per.html;/home/aleksandar/Zotero/storage/YMHUDVZ3/per.html},
  journal = {European Journal of Personality},
  keywords = {assessment,DONE,measurement,network analysis,personality scales and inventories,validity},
  language = {en},
  number = {n/a}
}

@techreport{christensenRandomFactorNetwork2020,
  title = {Random, {{Factor}}, or {{Network Model}}? {{Predictions From Neural Networks}}},
  shorttitle = {Random, {{Factor}}, or {{Network Model}}?},
  author = {Christensen, Alexander P. and Golino, Hudson},
  year = {2020},
  month = aug,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/awkcb},
  abstract = {The nature of associations between variables is important for constructing theory about psychological phenomena. In the last decade, this topic has received renewed interested with the introduction of psychometric network models. In psychology, these models are often contrasted with latent variable (e.g., factor) models. Recent research has shown that differences between the two tend to be more substantive than statistical. One recently developed algorithm called the Loadings Comparison Test (LCT) was developed to predict whether data were generated from a random, factor, or network model. A significant limitation of current LCT implementation is that it's based on heuristics that were derived from descriptive statistics. In the present study, we used artificial neural networks to replace these heuristics, and develop a more robust and generalizable algorithm. We performed a simulation study that compared neural networks to the original LCT algorithm as well as logistic regression models that were trained on the same data. We found that the neural networks performed as well as or better than both methods, demonstrating generalizablity across data generating models. We echo the call for more formal theories about the relations between variables and discuss the role of the LCT in this process.},
  file = {/home/aleksandar/Zotero/storage/5ZVT3BEB/Christensen and Golino - 2020 - Random, Factor, or Network Model Predictions From.pdf},
  language = {en},
  type = {Preprint}
}

@techreport{christensenStatisticalEquivalencyFactor2020,
  title = {Statistical {{Equivalency}} of {{Factor}} and {{Network Loadings}}},
  author = {Christensen, Alexander P. and Golino, Hudson},
  year = {2020},
  month = jan,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/xakez},
  abstract = {Recent research has demonstrated that the network measure node strength or the sum of a node's connections is statistically redundant with confirmatory factor analysis (CFA) factor loadings. A key finding of these studies is that node strength represents a combination of different latent causes. In the present study, we sought to circumvent this issue by formulating a network equivalent of factor loadings called network loadings. Using a Monte Carlo simulation approach, we evaluate the accuracy of network, CFA, and exploratory factor analysis (EFA) loadings to estimate the population loading matrix. Our results demonstrate that the network loadings can effectively estimate the population factor loadings and are more similar to EFA loadings than CFA loadings. This suggests that network loadings can provide similar information as factor loadings and therefore can be used in a similar way. Specifically, network loadings can be used as an item selection criterion. Moreover, they open avenues to performing measurement invariance analyses and computing network scores (or the network equivalent of factor scores).},
  keywords = {TODO},
  language = {en},
  type = {Preprint}
}

@techreport{christensenUniqueVariableAnalysis2020,
  title = {Unique Variable Analysis: {{A}} Novel Approach for Detecting Redundant Variables in Multivariate Data},
  shorttitle = {Unique Variable Analysis},
  author = {Christensen, Alexander P. and Garrido, Luis Eduardo and Golino, Hudson},
  year = {2020},
  month = dec,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/4kra2},
  abstract = {One common approach for constructing tests that measure a single attribute is the semantic similarity approach where items vary slightly in their wording and content. Despite being an effective strategy for ensuring high internal consistency, the information in tests may become redundant or worse confound the interpretation of the test scores. With the advent of network models, where tests represent a complex system and components (usually items) represent causally autonomous features, redundant variables may have inadvertent effects on the interpretation of their metrics. These issues motivated the development of a novel approach called Unique Variable Analysis (UVA), which detects redundant variables in multivariate data. The goal of UVA is to statistically identify potential redundancies in multivariate data so that researchers can make decisions about how best to handle them. Using a Monte Carlo simulation approach, we generated multivariate data with redundancies that were based on examples of known real-world redundancies. We then demonstrate the effects that redundancy can have on the accurate estimation of dimensions. Next, we evaluated UVA's ability to detect redundant variables in the simulated data. Based on these results, we provide a tutorial for how to apply UVA to real-world data. Our example data demonstrate that redundant variables create inaccurate estimates of dimensional structure but after applying UVA, the expected structure can be recovered. In sum, our study suggests that redundancy can have substantial effects on validity if left unchecked and that redundancy assessment should be integrated into standard validation practices.},
  file = {/home/aleksandar/Zotero/storage/J9IQ5RF5/Christensen et al_2020_Unique variable analysis.pdf},
  keywords = {minor factors,Psychometrics,Quantitative Methods,redundancy,Social and Behavioral Sciences,validity}
}

@article{chunSocialConformityCreates,
  title = {Social {{Conformity Creates Consensus}} and {{Strong Diversity}} of {{Hegselmann}}-{{Krause Opinion Dynamics}}},
  author = {Chun, Cheng and Changbin, Yu},
  publisher = {{Science China Press}},
  issn = {1674--733, 1869-1919},
  doi = {10.1007/s11432-019-2788-5},
  abstract = {This paper introduces a novel opinion dynamics model consisting of bounded confidence and conformity pressure. By applying conformity theory on Hegselmann-Krause (H-K) model, our model can capture two types of social conformity behaviors: compliance and acceptance. We assume that the inner non-conformity and public conformity of agents create different private and expressed opinions in compliance case, while both inner and public responses are conformity in acceptance case. The resulting dynamics in our model is substantially different from that in classic bounded confidence model. We find that: (1) in acceptance case, the opinions of all agents can always reach a consensus in bounded time; (2)in compliance case, both consensus and strong diversity of expressed opinions can be observed. We also prove that if the neighbor set of all agents are non-empty, the dynamics converge to consensus, which is only sufficient under such condition.},
  file = {/home/aleksandar/Dropbox/Zotero/Chun_Changbin/Chun_Changbin_Social Conformity Creates Consensus and Strong Diversity of Hegselmann-Krause.pdf;/home/aleksandar/Zotero/storage/DB8RCTAR/s11432-019-2788-5.html},
  journal = {SCIENCE CHINA Information Sciences},
  language = {en}
}

@article{chvykovCausalGeometry2020,
  title = {Causal {{Geometry}}},
  author = {Chvykov, Pavel and Hoel, Erik},
  year = {2020},
  month = oct,
  abstract = {Information geometry has offered a way to formally study the efficacy of scientific models by quantifying the impact of model parameters on the predicted effects. However, there has been little formal investigation of causation in this framework, despite causal models being a fundamental part of science and explanation. Here we introduce causal geometry, which formalizes not only how outcomes are impacted by parameters, but also how the parameters of a model can be intervened upon. Therefore we introduce a geometric version of "effective information"---a known measure of the informativeness of a causal relationship. We show that it is given by the matching between the space of effects and the space of interventions, in the form of their geometric congruence. Therefore, given a fixed intervention capability, an effective causal model is one that matches those interventions. This is a consequence of "causal emergence," wherein macroscopic causal relationships may carry more information than "fundamental" microscopic ones. We thus argue that a coarse-grained model may, paradoxically, be more informative than the microscopic one, especially when it better matches the scale of accessible interventions---as we illustrate on toy examples.},
  archivePrefix = {arXiv},
  eprint = {2010.09390},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Chvykov_Hoel/Chvykov_Hoel_2020_Causal Geometry.pdf;/home/aleksandar/Zotero/storage/UKSLVM35/2010.html},
  journal = {arXiv:2010.09390 [hep-th, physics:physics]},
  keywords = {Computer Science - Information Theory,High Energy Physics - Theory,Physics - Data Analysis; Statistics and Probability},
  primaryClass = {hep-th, physics:physics}
}

@article{cieciuchComparingResultsExact2014,
  title = {Comparing Results of an Exact vs. an Approximate ({{Bayesian}}) Measurement Invariance Test: A Cross-Country Illustration with a Scale to Measure 19 Human Values},
  shorttitle = {Comparing Results of an Exact vs. an Approximate ({{Bayesian}}) Measurement Invariance Test},
  author = {Cieciuch, Jan and Davidov, Eldad and Schmidt, Peter and Algesheimer, Ren{\~A}{\textcopyright} and Schwartz, Shalom H.},
  year = {2014},
  month = sep,
  volume = {5},
  issn = {1664-1078},
  doi = {10.3389/fpsyg.2014.00982},
  file = {/home/aleksandar/Dropbox/Zotero/Cieciuch et al/Cieciuch et al_2014_Comparing results of an exact vs.pdf;/home/aleksandar/Dropbox/Zotero/Cieciuch et al/Cieciuch et al_2014_Comparing results of an exact vs2.pdf},
  journal = {Frontiers in Psychology},
  language = {en}
}

@article{cieciuchNumberDistinctBasic2012,
  ids = {cieciuchNumberDistinctBasic2012a},
  title = {The {{Number}} of {{Distinct Basic Values}} and {{Their Structure Assessed}} by {{PVQ}}\textendash 40},
  author = {Cieciuch, Jan and Schwartz, Shalom H.},
  year = {2012},
  month = may,
  volume = {94},
  pages = {321--328},
  issn = {0022-3891, 1532-7752},
  doi = {10.1080/00223891.2012.655817},
  file = {/home/aleksandar/Dropbox/Zotero/Cieciuch_Schwartz/Cieciuch_Schwartz_2012_The Number of Distinct Basic Values and Their Structure Assessed by PVQ–2.pdf;/home/aleksandar/Dropbox/Zotero/Cieciuch_Schwartz/Cieciuch_Schwartz_2012_The Number of Distinct Basic Values and Their Structure Assessed by PVQ–40.pdf},
  journal = {Journal of Personality Assessment},
  language = {en},
  number = {3}
}

@article{cieciuchTestingApproximateMeasurement2018,
  title = {Testing for {{Approximate Measurement Invariance}} of {{Human Values}} in the {{European Social Survey}}},
  author = {Cieciuch, Jan and Davidov, Eldad and Algesheimer, Ren{\'e} and Schmidt, Peter},
  year = {2018},
  month = nov,
  volume = {47},
  pages = {665--686},
  issn = {0049-1241, 1552-8294},
  doi = {10.1177/0049124117701478},
  journal = {Sociological Methods \& Research},
  language = {en},
  number = {4}
}

@article{ciminelliSocialNetworkSpatial,
  title = {Social Network Spatial Model},
  author = {Ciminelli, Joseph T. and Love, Tanzy and Wu, Tong Tong},
  doi = {10.1016/j.spasta.2018.11.001},
  abstract = {{$<$}h2{$>$}Abstract{$<$}/h2{$><$}p{$>$}Our work is motivated by a desire to incorporate the vast wealth of social network data into the framework of spatial models. We introduce a method for modeling the spatial correlations that exist over a social network. In particular, we model attributes measured for each member of the network as a continuous process over the social space created by their connections. Our method simultaneously models the unobserved locations of network members in social space and the spatial process that exists over that space based on the observed network connections and nodal attributes. The model is evaluated through simulation studies and applied to the importance ranking for a network of emergency response organizations and the physical activity habits of teenage girls. The introduced methods incorporate network data into the spatial framework, expanding traditional models to include this often relevant source of additional information.{$<$}/p{$>$}},
  journal = {Spatial Statistics}
}

@article{ciminelliSocialNetworkSpatiala,
  title = {Social Network Spatial Model},
  author = {Ciminelli, Joseph T. and Love, Tanzy and Wu, Tong Tong},
  doi = {10.1016/j.spasta.2018.11.001},
  abstract = {{$<$}h2{$>$}Abstract{$<$}/h2{$><$}p{$>$}Our work is motivated by a desire to incorporate the vast wealth of social network data into the framework of spatial models. We introduce a method for modeling the spatial correlations that exist over a social network. In particular, we model attributes measured for each member of the network as a continuous process over the social space created by their connections. Our method simultaneously models the unobserved locations of network members in social space and the spatial process that exists over that space based on the observed network connections and nodal attributes. The model is evaluated through simulation studies and applied to the importance ranking for a network of emergency response organizations and the physical activity habits of teenage girls. The introduced methods incorporate network data into the spatial framework, expanding traditional models to include this often relevant source of additional information.{$<$}/p{$>$}},
  journal = {Spatial Statistics}
}

@article{claggettNationalizationAmericanElectorate1984,
  title = {Nationalization of the {{American}} Electorate},
  author = {Claggett, William and Flanigan, William and Zingale, Nancy},
  year = {1984},
  month = mar,
  volume = {78},
  pages = {77--91},
  issn = {0003-0554, 1537-5943},
  doi = {10.2307/1961250},
  journal = {American Political Science Review},
  language = {en},
  number = {01}
}

@article{claudioStatisticalPhysicsSocial2009,
  title = {Statistical Physics of Social Dynamics},
  author = {Claudio, Castellano and Santo, Fortunato and Vittorio, Loreto},
  year = {2009},
  abstract = {Statistical physics has proven to be a fruitful framework to describe phenomena outside the realm of traditional physics. Recent years have witnessed an attempt by physicists to study collective phenomena emerging from the interactions of individuals as elementary units in social structures. A wide list of topics are reviewed ranging from opinion and cultural and language dynamics to crowd behavior, hierarchy formation, human dynamics, and social spreading. The connections between these problems and other, more traditional, topics of statistical physics are highlighted. Comparison of model results with empirical data from social systems are also emphasized.}
}

@article{coffeExplainingGenderDifferences,
  title = {Explaining {{Gender Differences}} in {{Turnout Using Panel Data Across Elections}}},
  author = {Coff{\'e}, Hilde and Lago, Ignacio},
  volume = {n/a},
  issn = {1540-6237},
  doi = {10.1111/ssqu.12759},
  abstract = {Objectives The current study offers the first systematic analysis of the impact of citizens' interest in and perceptions of specific elections on gender differences in turnout. Methods Using an Internet panel survey conducted by the Making Electoral Democracy Work project covering the 2013 state, 2013 federal, and 2014 European elections in Bavaria (Germany), our probit models examine mediation and moderation effects of three election-related characteristics: the issues that citizens consider most central in the elections, citizens' interest in the elections, and the perceived influence among citizens of the policies of the different levels of policy making on their well-being. Results The results indicate an overall gender difference in turnout in Bavaria with women being less likely to vote compared with men. Yet, this gender effect ceases to be significant once citizens' attitudes toward and perceptions of specific elections are controlled for. Interest in the election at hand has a particularly strong and positive effect on the likelihood of voting. We find no significant gender interactions. Conclusion Citizens' interest in and perceptions of specific elections have a strong impact on turnout and gender differences therein.},
  copyright = {\textcopyright{} 2019 by the Southwestern Social Science Association},
  file = {/home/aleksandar/Zotero/storage/CSU9I86M/ssqu.html},
  journal = {Social Science Quarterly},
  language = {en},
  number = {n/a}
}

@article{cohenPartyPolicyDominating2003,
  title = {Party over Policy: {{The}} Dominating Impact of Group Influence on Political Beliefs.},
  shorttitle = {Party over Policy},
  author = {Cohen, Geoffrey L.},
  year = {2003},
  volume = {85},
  pages = {808},
  publisher = {{American Psychological Association}},
  journal = {Journal of personality and social psychology},
  number = {5}
}

@article{colemanProcessesConcentrationDispersal1974,
  title = {Processes of Concentration and Dispersal of Power in Social Systems},
  author = {Coleman, J. S.},
  year = {1974},
  month = apr,
  volume = {13},
  pages = {7--18},
  issn = {0539-0184},
  doi = {10.1177/053901847401300201},
  journal = {Social Science Information},
  language = {en},
  number = {2}
}

@article{colemanVotingConformityRussia,
  title = {Voting and Conformity: {{Russia}}, 1993\textendash 2016},
  author = {Coleman, Stephen},
  doi = {S0165489617301385},
  abstract = {Past research has showed the strong influence of social norms and conformity on voting choices in the national elections of many countries, with some more affected than others. This research demonstrates that social conformity has strongly and increasingly affected parliamentary elections in Russia. The analysis tests and estimates a mathematical model of the collective effect of conformity on people's voting choices in each parliamentary election from 1993 to 2016 at the regional level using cross-sectional regression analysis. The analysis shows that voting and the party system are approaching the typical voting patterns seen in many democracies. Remarkably, voters typically are not aware of the influence of conformity on their own voting decisions.},
  journal = {Mathematical Social Science}
}

@article{colemanVotingConformityRussiaa,
  title = {Voting and Conformity: {{Russia}}, 1993\textendash 2016},
  author = {Coleman, Stephen},
  doi = {S0165489617301385},
  abstract = {Past research has showed the strong influence of social norms and conformity on voting choices in the national elections of many countries, with some more affected than others. This research demonstrates that social conformity has strongly and increasingly affected parliamentary elections in Russia. The analysis tests and estimates a mathematical model of the collective effect of conformity on people's voting choices in each parliamentary election from 1993 to 2016 at the regional level using cross-sectional regression analysis. The analysis shows that voting and the party system are approaching the typical voting patterns seen in many democracies. Remarkably, voters typically are not aware of the influence of conformity on their own voting decisions.},
  journal = {Mathematical Social Science}
}

@article{collignonRiskElectoralDefeat,
  title = {Risk of Electoral Defeat and Heterogeneous Decentralisation},
  author = {Collignon, Sofia},
  pages = {45},
  abstract = {In the last three decades several countries around the word have transferred authority from their national to their regional governments. However, not all their regions have been empowered in the same magnitude and important differences can be observed between and within countries. Why do some regions obtain more power than others? Current literature argues that variation in the redistribution of power and resources between regions is introduced by demand. Yet, these explanations are conditioned on the presence of strong regionalist parties or territorial cleavages. This article instead proposes a theory that links the government's risk of future electoral defeat with heterogeneous decentralisation, and tests its effects using data from 15 European countries and 141 regions. The results provide evidence that parties in government protect themselves against the risk of electoral defeat by selectively targeting decentralisation towards regions in which they are politically strong. The findings challenge previous research that overestimate the importance of regionalist parties while overlooking differences between regions.},
  file = {/home/aleksandar/Dropbox/Zotero/Collignon/Collignon_Risk of electoral defeat and heterogeneous decentralisation.pdf},
  language = {en}
}

@article{CollinearityIsnDisease,
  title = {Collinearity Isn't a Disease That Needs Curing},
  pages = {15},
  abstract = {Once they have learnt about the effects of collinearity on the output of multiple regression models, researchers may unduly worry about these and resort to dubious modelling techniques to mitigate them. This article argues that, to the extent that problems occur in the presence of collinearity, they are not caused by it but rather by common mental shortcuts that researchers take when interpreting statistical model that can also lead them astray in the absence of collinearity. Moreover, I illustrate that common strategies for dealing with collinearity only sidestep the perceived problem by biasing 5 the parameters, reformulating the model in such a way that it maps onto different research questions, or both. I conclude that collinearity in itself is not a problem and that researchers should be aware of what their approaches for addressing it actually achieve.},
  file = {/home/aleksandar/Dropbox/Zotero/undefined/Collinearity isn't a disease that needs curing.pdf},
  language = {en}
}

@article{colomboLearningHighdimensionalDirected2012,
  title = {Learning High-Dimensional Directed Acyclic Graphs with Latent and Selection Variables},
  author = {Colombo, Diego and Maathuis, Marloes H. and Kalisch, Markus and Richardson, Thomas S.},
  year = {2012},
  month = feb,
  volume = {40},
  pages = {294--321},
  issn = {0090-5364},
  doi = {10.1214/11-AOS940},
  abstract = {We consider the problem of learning causal information between random variables in directed acyclic graphs (DAGs) when allowing arbitrarily many latent and selection variables. The FCI (Fast Causal Inference) algorithm has been explicitly designed to infer conditional independence and causal information in such settings. However, FCI is computationally infeasible for large graphs. We therefore propose the new RFCI algorithm, which is much faster than FCI. In some situations the output of RFCI is slightly less informative, in particular with respect to conditional independence information. However, we prove that any causal information in the output of RFCI is correct in the asymptotic limit. We also define a class of graphs on which the outputs of FCI and RFCI are identical. We prove consistency of FCI and RFCI in sparse high-dimensional settings, and demonstrate in simulations that the estimation performances of the algorithms are very similar. All software is implemented in the R-package pcalg.},
  archivePrefix = {arXiv},
  eprint = {1104.5617},
  eprinttype = {arxiv},
  journal = {The Annals of Statistics},
  number = {1}
}

@article{colomerComparingImportancePolitical,
  title = {Comparing the {{Importance}} of {{Political Offices}}: {{A Position Prominence Index}}},
  author = {Colomer, Josep and Sikk, Allan and Taagepera, Rein},
  pages = {29},
  abstract = {We propose a simple yet highly insightful index of Position Prominence (PP) to measure the importance of different political offices. PP allows us to compare offices within and between countries as well as between different levels of government. We conduct a preliminary test of the measure by looking at movement of people between different offices in the European Union in the United States. By and large, people move from offices with lower PP levels to those with higher PP levels and significant exceptions can be explained by specific circumstances. We also outline a broader research agenda for the use of this novel measure.},
  language = {en}
}

@article{comissoGlassHalfFull1997,
  title = {Is the Glass Half Full or Half Empty?},
  author = {Comisso, Ellen},
  year = {1997},
  month = mar,
  volume = {30},
  pages = {1--21},
  issn = {0967067X},
  doi = {10.1016/S0967-067X(96)00020-7},
  journal = {Communist and Post-Communist Studies},
  language = {en},
  number = {1}
}

@misc{ConditionalProbabilityTheory,
  title = {Conditional {{Probability Theory}} ({{For Scientists}} and {{Engineers}})},
  file = {/home/aleksandar/Zotero/storage/32E2V9BC/conditional_probability_theory.html},
  howpublished = {https://betanalpha.github.io/assets/case\_studies/conditional\_probability\_theory.html}
}

@misc{ConnectedPapersFind,
  title = {Connected {{Papers}} | {{Find}} and Explore Academic Papers},
  abstract = {A unique, visual tool to help researchers and applied scientists find and explore papers relevant to their field of work.},
  howpublished = {https://www.connectedpapers.com/},
  language = {en}
}

@inproceedings{contiAnalysisTrendsConnections2020,
  title = {An {{Analysis}} of {{Trends}} and {{Connections}} in {{Google}}, {{Twitter}}, and {{Wikipedia}}},
  booktitle = {{{HCI International}} 2020 - {{Posters}}},
  author = {Conti, Gianluca and Sansonetti, Giuseppe and Micarelli, Alessandro},
  editor = {Stephanidis, Constantine and Antona, Margherita},
  year = {2020},
  pages = {154--160},
  publisher = {{Springer International Publishing}},
  address = {{Cham}},
  doi = {10.1007/978-3-030-50732-9_21},
  abstract = {In this paper, we propose a system for extracting, storing, and analyzing the data provided by three well-known and widespread services available online. More specifically, the system can automatically collect a real-world dataset for a selected language and/or geographical region and match similar trends expressed through different keywords. Unlike previous studies in the same area, we avoided to focus on a specific aspect and explored which resonance different topics may have between one source and another, and how quickly each source generally reacts to external events.},
  isbn = {978-3-030-50732-9},
  language = {en},
  series = {Communications in {{Computer}} and {{Information Science}}}
}

@article{conversePoliticizationElectorateFrance21,
  title = {Politicization of the Electorate in {{France}} and the {{United States}}},
  author = {Converse, Philip E. and Dupeux, Georges},
  year = {21},
  volume = {26},
  pages = {1},
  issn = {0033362X},
  doi = {10.1086/267067},
  journal = {Public Opinion Quarterly},
  number = {1}
}

@article{cordobaReviewGaussianMarkov2020,
  title = {A Review of {{Gaussian Markov}} Models for Conditional Independence},
  author = {C{\'o}rdoba, Irene and Bielza, Concha and Larra{\~n}aga, Pedro},
  year = {2020},
  month = may,
  volume = {206},
  pages = {127--144},
  issn = {0378-3758},
  doi = {10.1016/j.jspi.2019.09.008},
  abstract = {Markov models lie at the interface between statistical independence in a probability distribution and graph separation properties. We review model selection and estimation in directed and undirected Markov models with Gaussian parametrization, emphasizing the main similarities and differences. These two model classes are similar but not equivalent, although they share a common intersection. We present the existing results from a historical perspective, taking into account the amount of literature existing from both the artificial intelligence and statistics research communities, where these models were originated. We cover classical topics such as maximum likelihood estimation and model selection via hypothesis testing, but also more modern approaches like regularization and Bayesian methods. We also discuss how the Markov models reviewed fit in the rich hierarchy of other, higher level Markov model classes. Finally, we close the paper overviewing relaxations of the Gaussian assumption and pointing out the main areas of application where these Markov models are nowadays used.},
  file = {/home/aleksandar/Dropbox/Zotero/Córdoba et al/Córdoba et al_2020_A review of Gaussian Markov models for conditional independence.pdf},
  journal = {Journal of Statistical Planning and Inference},
  language = {en}
}

@article{cornacchiaPolarizationAttractionRepulsionModels2020,
  title = {Polarization in {{Attraction}}-{{Repulsion Models}}},
  author = {Cornacchia, Elisabetta and Singer, Neta and Abbe, Emmanuel},
  year = {2020},
  month = jun,
  abstract = {This paper introduces a model for opinion dynamics, where at each time step, randomly selected agents see their opinions - modeled as scalars in [0,1] - evolve depending on a local interaction function. In the classical Bounded Confidence Model, agents opinions get attracted when they are close enough. The proposed model extends this by adding a repulsion component, which models the effect of opinions getting further pushed away when dissimilar enough. With this repulsion component added, and under a repulsion-attraction cleavage assumption, it is shown that a new stable configuration emerges beyond the classical consensus configuration, namely the polarization configuration. More specifically, it is shown that total consensus and total polarization are the only two possible limiting configurations. The paper further provides an analysis of the infinite population regime in dimension 1 and higher, with a phase transition phenomenon conjectured and backed heuristically.},
  archivePrefix = {arXiv},
  eprint = {2006.05251},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Dropbox/Zotero/Cornacchia et al/Cornacchia et al_2020_Polarization in Attraction-Repulsion Models.pdf;/home/aleksandar/Zotero/storage/DZE4YXVK/2006.html},
  journal = {arXiv:2006.05251 [cs, math]},
  primaryClass = {cs, math}
}

@article{cornellBureaucracyGrowth2020,
  title = {Bureaucracy and {{Growth}}},
  author = {Cornell, Agnes and Knutsen, Carl Henrik and Teorell, Jan},
  year = {2020},
  month = apr,
  pages = {0010414020912262},
  publisher = {{SAGE Publications Inc}},
  issn = {0010-4140},
  doi = {10.1177/0010414020912262},
  abstract = {We revisit the hypothesis that a Weberian bureaucracy enhances economic growth. Theoretically, we develop arguments for why such a bureaucracy may enhance growth and discuss plausible counterarguments. Empirically, we use new measures capturing various Weberian features in countries across the world, with some time series extending back to 1789. The evidence base from previous large-N studies is surprisingly thin, but our extensive data enable us to move beyond the problematic cross-country correlations used in previous studies. Hence, we conduct tests that control for country-specific characteristics while ensuring sufficient variation on the slow-moving bureaucracy variables to enable precise estimation. Our analysis suggests that previous cross-country regressions have vastly overstated the strength of the relationship. While this casts uncertainty on the proposition that there is an effect of Weberian bureaucracy on growth, our further analysis suggests that\textemdash if an effect exists\textemdash it may operate in the short term and be stronger in recent decades.},
  file = {/home/aleksandar/Dropbox/Zotero/Cornell et al/Cornell et al_2020_Bureaucracy and Growth.pdf},
  journal = {Comparative Political Studies},
  language = {en}
}

@article{cornesseReviewConceptualApproaches,
  title = {A {{Review}} of {{Conceptual Approaches}} and {{Empirical Evidence}} on {{Probability}} and {{Nonprobability Sample Survey Research}}},
  author = {Cornesse, Carina and Blom, Annelies G. and Dutwin, David and Krosnick, Jon A. and De Leeuw, Edith D. and Legleye, St{\'e}phane and Pasek, Josh and Pennay, Darren and Phillips, Benjamin and Sakshaug, Joseph W. and Struminskaya, Bella and Wenz, Alexander},
  doi = {10.1093/jssam/smz041},
  abstract = {Abstract.  There is an ongoing debate in the survey research literature about whether and when probability and nonprobability sample surveys produce accurate es},
  journal = {Journal of Survey Statistics and Methodology},
  language = {en}
}

@book{cornwellSocialSequenceAnalysis2015,
  title = {Social Sequence Analysis: Methods and Applications},
  shorttitle = {Social Sequence Analysis},
  author = {Cornwell, Benjamin},
  year = {2015},
  publisher = {{Cambridge University Press}},
  address = {{New York, NY}},
  isbn = {978-1-107-10250-7 978-1-107-50054-9},
  lccn = {H61 .C657 2015},
  series = {Structural Analysis in the Social Sciences}
}

@article{corominaEvaluatingMeasurementInvariance2013,
  title = {Evaluating Measurement Invariance for Social and Political Trust in Western Europe over Four Measurement Time Points (2002 - 2008)},
  author = {Coromina, Lluis and Davidov, Eldad},
  year = {2013},
  publisher = {{IFiS Publishers}},
  doi = {10.5167/UZH-92161}
}

@article{cosciaAtlasAspiringNetwork2021,
  title = {The {{Atlas}} for the {{Aspiring Network Scientist}}},
  author = {Coscia, Michele},
  year = {2021},
  month = jan,
  abstract = {Network science is the field dedicated to the investigation and analysis of complex systems via their representations as networks. We normally model such networks as graphs: sets of nodes connected by sets of edges and a number of node and edge attributes. This deceptively simple object is the starting point of never-ending complexity, due to its ability to represent almost every facet of reality: chemical interactions, protein pathways inside cells, neural connections inside the brain, scientific collaborations, financial relations, citations in art history, just to name a few examples. If we hope to make sense of complex networks, we need to master a large analytic toolbox: graph and probability theory, linear algebra, statistical physics, machine learning, combinatorics, and more. This book aims at providing the first access to all these tools. It is intended as an "Atlas", because its interest is not in making you a specialist in using any of these techniques. Rather, after reading this book, you will have a general understanding about the existence and the mechanics of all these approaches. You can use such an understanding as the starting point of your own career in the field of network science. This has been, so far, an interdisciplinary endeavor. The founding fathers of this field come from many different backgrounds: mathematics, sociology, computer science, physics, history, digital humanities, and more. This Atlas is charting your path to be something different from all of that: a pure network scientist.},
  archivePrefix = {arXiv},
  eprint = {2101.00863},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Zotero/storage/7I5XHW7F/Coscia_2021_The Atlas for the Aspiring Network Scientist.pdf;/home/aleksandar/Zotero/storage/Y4YJFQUY/2101.html},
  journal = {arXiv:2101.00863 [physics]},
  keywords = {Computer Science - Computers and Society,Computer Science - Social and Information Networks,Physics - Data Analysis; Statistics and Probability,Physics - Physics and Society},
  primaryClass = {physics}
}

@article{cosciaDiscoveringCommunitiesCommunity2019,
  title = {Discovering {{Communities}} of {{Community Discovery}}},
  author = {Coscia, Michele},
  year = {2019},
  month = jul,
  abstract = {Discovering communities in complex networks means grouping nodes similar to each other, to uncover latent information about them. There are hundreds of different algorithms to solve the community detection task, each with its own understanding and definition of what a "community" is. Dozens of review works attempt to order such a diverse landscape -- classifying community discovery algorithms by the process they employ to detect communities, by their explicitly stated definition of community, or by their performance on a standardized task. In this paper, we classify community discovery algorithms according to a fourth criterion: the similarity of their results. We create an Algorithm Similarity Network (ASN), whose nodes are the community detection approaches, connected if they return similar groupings. We then perform community detection on this network, grouping algorithms that consistently return the same partitions or overlapping coverage over a span of more than one thousand synthetic and real world networks. This paper is an attempt to create a similarity-based classification of community detection algorithms based on empirical data. It improves over the state of the art by comparing more than seventy approaches, discovering that the ASN contains well-separated groups, making it a sensible tool for practitioners, aiding their choice of algorithms fitting their analytic needs.},
  archivePrefix = {arXiv},
  eprint = {1907.02277},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Zotero/storage/WKTEKW98/1907.html},
  journal = {arXiv:1907.02277 [physics]},
  primaryClass = {physics}
}

@article{costantiniStabilityVariabilityPersonality2019a,
  ids = {costantiniStabilityVariabilityPersonality2019},
  title = {Stability and Variability of Personality Networks. {{A}} Tutorial on Recent Developments in Network Psychometrics},
  author = {Costantini, Giulio and Richetin, Juliette and Preti, Emanuele and Casini, Erica and Epskamp, Sacha and Perugini, Marco},
  year = {2019},
  month = jan,
  volume = {136},
  pages = {68--78},
  issn = {01918869},
  doi = {10.1016/j.paid.2017.06.011},
  journal = {Personality and Individual Differences},
  language = {en}
}

@article{costantiniStateARtPersonality2015a,
  ids = {costantiniStateARtPersonality2015,costantini\_state\_2015-1},
  title = {State of the {{aRt}} Personality Research: {{A}} Tutorial on Network Analysis of Personality Data in {{R}}},
  shorttitle = {State of the {{aRt}} Personality Research},
  author = {Costantini, Giulio and Epskamp, Sacha and Borsboom, Denny and Perugini, Marco and M{\~o}ttus, Ren{\'e} and Waldorp, Lourens J. and Cramer, Ang{\'e}lique O.J.},
  year = {2015},
  month = feb,
  volume = {54},
  pages = {13--29},
  issn = {00926566},
  doi = {10.1016/j.jrp.2014.07.003},
  journal = {Journal of Research in Personality},
  language = {en}
}

@article{coulterDeterminantsTrustService2002,
  title = {Determinants of Trust in a Service Provider: The Moderating Role of Length of Relationship},
  shorttitle = {Determinants of Trust in a Service Provider},
  author = {Coulter, Keith S. and Coulter, Robin A.},
  year = {2002},
  month = jan,
  volume = {16},
  pages = {35--50},
  publisher = {{MCB UP Ltd}},
  issn = {0887-6045},
  doi = {10.1108/08876040210419406},
  abstract = {Trust is a key factor in the establishment of long-term relationships between service representatives and their customers. Prior research has documented that both ``person-related'' (e.g. empathy, politeness and customer/service representative similarity) and ``offer-related'' (customization, competence, reliability and promptness) service representative characteristics have an impact on trust. However, the relative importance of these characteristics, and in some cases the direction of their relationships with trust, has varied across studies. In this paper, we posit a contingency model of trust, suggesting that the effects of the above variables on trust are moderated by length of the customer/service provider relationship. Our model is tested in a business-to-business context by means of a mail survey involving 677 small business owners. The small business owners provided data about their relationships with their insurance industry service providers. Our results demonstrate how service representatives and firms can work toward the establishment of trust with their clients under varying market conditions.},
  file = {/home/aleksandar/Documents/Zotero/Coulter_Coulter/Coulter_Coulter_2002_Determinants of trust in a service provider.pdf},
  journal = {Journal of Services Marketing},
  number = {1}
}

@article{counsellEvaluatingEquivalenceTesting,
  ids = {counsellEvaluatingEquivalenceTestinga},
  title = {Evaluating {{Equivalence Testing Methods}} for {{Measurement Invariance}}},
  author = {Counsell, Alyssa and Cribbie, Robert A. and Flora, David B.},
  doi = {10.1080/00273171.2019.1633617},
  abstract = {Measurement Invariance (MI) is often concluded from a nonsignificant chi-square difference test. Researchers have also proposed using change in goodness-of-fit indices ({$<$}math{$>\Delta<$}/math{$>$}GOFs) instead. Both of these commonly used methods for testing MI have important limitations. To combat these issues, To combat these issues, it was proposed using an equivalence test (EQ) to replace the chi-square difference test commonly used to test MI. Due to concerns with the EQ's power, and adjusted version (EQ-A) was created, but provides little evaluation of either procedure. The current study evaluated the Type I error and power of both the EQ and EQ-A, and compared their performance to that of the traditional chi-square difference test and {$<$}math{$>\Delta<$}/math{$>$}GOFs. The EQ was the only procedure that maintained empirical error rates below the nominal alpha level. Results also highlight that the EQ requires larger sample sizes than traditional difference-based approaches or using equivalence bounds based on larger than conventional RMSEA values (e.g., \> .05) to ensure adequate power rates. We do not recommend the proposed adjustment (EQ-A) over the EQ.},
  journal = {Multivariate Behavioral Research}
}

@article{coxElectoralRulesElectoral1999,
  title = {Electoral Rules and Electoral Coordination},
  author = {Cox, G.},
  year = {1999},
  month = jun,
  volume = {2},
  pages = {145--161},
  issn = {1094-2939, 1545-1577},
  doi = {10.1146/annurev.polisci.2.1.145},
  journal = {Annual Review of Political Science},
  language = {en},
  number = {1}
}

@inproceedings{coxEthnesFiscsElectoral2003,
  title = {Ethnes, Fiscs, and Electoral {{Rules}}: {{The}} Determinants of Party-System Inflation},
  booktitle = {Annual {{Meeting}} of the {{American Political Science Association}}},
  author = {Cox, G. and Knoll, J.},
  year = {2003},
  address = {{Chicago}}
}

@article{coxMeasuringCompetitivenessElections,
  title = {Measuring the {{Competitiveness}} of {{Elections}}},
  author = {Cox, Gary W. and Fiva, Jon H. and Smith, Daniel M.},
  doi = {10.1017/pan.2019.28},
  abstract = {The concept of electoral competition plays a central role in many subfields of political science, but no consensus exists on how to measure it. One key challenge is how to conceptualize and measure electoral competitiveness at the district level across alternative electoral systems. Recent efforts to meet this challenge have introduced general measures of competitiveness which rest on explicit calculations about how votes translate into seats, but also implicit assumptions about how effort maps into votes (and how costly effort is). We investigate how assumptions about the effort-to-votes mapping affect the units in which competitiveness is best measured, arguing in favor of vote-share-denominated measures and against vote-share-per-seat measures. Whether elections under multimember proportional representation systems are judged more or less competitive than single-member plurality or runoff elections depends directly on the units in which competitiveness is assessed (and hence on assumptions about how effort maps into votes).},
  journal = {Political Analysis}
}

@article{craigWhoOwnsWhat,
  title = {Who {{Owns What}}, and {{Why}}? {{The Origins}} of {{Issue Ownership Beliefs}}},
  shorttitle = {Who {{Owns What}}, and {{Why}}?},
  author = {Craig, Stephen C. and Cossette, Paulina S.},
  volume = {n/a},
  issn = {1747-1346},
  doi = {10.1111/polp.12338},
  abstract = {Most research on issue ownership examines the topic as an independent variable that may shape campaign strategy or voter behavior. In contrast, our study looks at ownership as a dependent variable. Specifically, we employ a national Internet survey of registered U.S. voters to gauge the extent to which such judgments are influenced by individual-level factors other than partisan identity. We find that while partisanship shapes ownership beliefs to a considerable degree, policy preferences and performance evaluations have significant effects across a range of valence and position issues. While feelings of ambivalence toward the in- or out-party do not have a consistent effect on ownership beliefs, higher out-party ambivalence reduces the probability that citizens will identify their own party as owning most issues. It is therefore premature, and probably incorrect, to assume that perceived ownership is little more than an expression of a person's partisan attachment or intended vote. Related Articles Bernick, Ethan M., and Nathan Myers. 2012. ``Issue Salience, Party Strength, and the Adoption of Health-Care Expansion Efforts.'' Politics \& Policy 40 (1): 131-159. https://doi.org/10.1111/j.1747-1346.2011.00340.x Grossmann, Matt. 2014. ``The Varied Effects of Policy Cues on Partisan Opinions.'' Politics \& Policy 42 (6): 881-904. https://doi.org/10.1111/polp.12102 Kim, Eun Kyung. 2019. ``Issue Ownership and Strategic Policy Choice in Multiparty Africa.'' Politics \& Policy 47 (5): 956-983. https://doi.org/10.1111/polp.12327 Related Media Brenan, Megan. 2019. ``Democrats Maintain Favorability Edge over Republicans.'' September 30. https://news.gallup.com/poll/267122/democrats-maintain-favorability-edge-republicans.aspx Hook, Janet. 2018. ``Tax Overhaul Remains Unpopular, Poll Shows.'' April 16. https://www.wsj.com/articles/tax-overhaul-remains-unpopular-poll-shows-1523912555 Pew Research Center. 2015. ``GOP's Favorability Rating Takes a Negative Turn.'' July 23. https://www.people-press.org/2015/07/23/gops-favorability-rating-takes-a-negative-turn/},
  copyright = {\textcopyright{} 2020 Policy Studies Organization},
  file = {/home/aleksandar/Zotero/storage/9XM62Z2T/polp.html},
  journal = {Politics \& Policy},
  language = {en},
  number = {n/a}
}

@article{cramDoesEUNeed2012,
  title = {Does the {{EU Need}} a {{Navel}}? {{Implicit}} and {{Explicit Identification}} with the {{European Union}}*},
  shorttitle = {Does the {{EU Need}} a {{Navel}}?},
  author = {Cram, Laura},
  year = {2012},
  volume = {50},
  pages = {71--86},
  issn = {1468-5965},
  doi = {10.1111/j.1468-5965.2011.02207.x},
  abstract = {Analysts should expect neither too much from European Union identity and its causal role in driving the integration process, nor too little, by underestimating the stabilizing force of implicit identification with the EU. Daily transactions in an EU institutional context embed an acceptance of the EU as a legitimate political authority and underpin passive consent to the continued functioning of the EU. The emergence of explicit EU identification is contingent upon the value (real and symbolic) attached to transactions, the extent to which valued goods are perceived to be under threat and whether competing political authorities are viewed as legitimate.},
  copyright = {\textcopyright{} 2011 The Author(s). JCMS: Journal of Common Market Studies \textcopyright{} 2011 Blackwell Publishing Ltd},
  file = {/home/aleksandar/Zotero/storage/KDT5AXXH/j.1468-5965.2011.02207.html},
  journal = {JCMS: Journal of Common Market Studies},
  language = {en},
  number = {1}
}

@article{crametonBulgarianElectionsDecember,
  title = {The {{Bulgarian Elections}} of {{December}} 1994},
  author = {CRAMeTON, RICHARD J},
  pages = {5},
  language = {en}
}

@article{cramptonBulgarianElections191997,
  title = {The {{Bulgarian}} Elections of 19 {{April}} 1997},
  author = {Crampton, Richard},
  year = {1997},
  month = dec,
  volume = {16},
  pages = {560--563},
  issn = {02613794},
  doi = {10.1016/S0261-3794(97)87816-X},
  journal = {Electoral Studies},
  language = {en},
  number = {4}
}

@book{craneProbabilisticFoundationsStatistical2018,
  title = {Probabilistic Foundations of Statistical Network Analysis},
  author = {Crane, Harry},
  year = {2018},
  publisher = {{CRC Press, Taylor \& Francis Group, a Chapman \& Hall book}},
  address = {{Boca Raton London New York}},
  isbn = {978-1-138-58599-7 978-1-138-63015-4},
  language = {eng},
  number = {157},
  series = {Monographs on Statistics and Applied Probability}
}

@article{craneTestBiasCognitive2004,
  title = {Test Bias in a Cognitive Test: Differential Item Functioning in the {{CASI}}},
  shorttitle = {Test Bias in a Cognitive Test},
  author = {Crane, Paul K. and van Belle, Gerald and Larson, Eric B.},
  year = {2004},
  month = jan,
  volume = {23},
  pages = {241--256},
  issn = {0277-6715, 1097-0258},
  doi = {10.1002/sim.1713},
  journal = {Statistics in Medicine},
  language = {en},
  number = {2}
}

@article{crawfordIdeologicalSymmetriesPrejudice2020,
  title = {Ideological ({{A}})Symmetries in Prejudice and Intergroup Bias},
  author = {Crawford, Jarret T and Brandt, Mark J},
  year = {2020},
  month = aug,
  volume = {34},
  pages = {40--45},
  issn = {2352-1546},
  doi = {10.1016/j.cobeha.2019.11.007},
  abstract = {The traditional perspective on the political ideology and prejudice relationship holds that political conservatism is associated with prejudice, and that the types of dispositional characteristics associated with conservatism (e.g. low cognitive ability, low Openness) explain this relationship. This conclusion is limited by the limited number and types of groups studied. When researchers use a more heterogeneous array of targets, people across the political spectrum express prejudice against groups with dissimilar values and beliefs. Evidence for this worldview conflict perspective emerges in both politics and religion, as well as individual differences such as Openness, disgust sensitivity and cognitive ability. Although these two perspectives differ substantially, there is some identifiable common ground between them, particularly the assumption of some psychological differences between liberals and conservatives. We discuss some remaining open questions related to worldview conflict reduction, causal processes, the robustness of the assumptions of the traditional perspective, and differences between political elites and the public.},
  file = {/home/aleksandar/Zotero/storage/E2UZ3FHM/S2352154619301251.html},
  journal = {Current Opinion in Behavioral Sciences},
  language = {en}
}

@techreport{crawfordTestingPredictiveValidity2018,
  title = {Testing the Predictive Validity of the Open-Minded Cognition Scale},
  author = {Crawford, Jarret and Brandt, Mark},
  year = {2018},
  month = feb,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/v8um2},
  abstract = {We identify both limitations to and important omissions in predictive validity tests of the open-minded cognition (OMC) scale, an indicator politically biased information processing. In five studies, we test two hypotheses derived from the conceptualization of OMC: the OMC outgroup attitude and OMC bias attenuation hypotheses. We find that whereas OMC is associated with less prejudice overall, evidence is mixed that it reduces prejudice towards attitudinally dissimilar groups, a key aspect of the OMC outgroup attitude hypothesis. We find mixed support for the OMC bias attenuation hypothesis: whereas OMC is associated with avoiding selective exposure to political information, it did not attenuate political biases across seven separate tests. We recommend further tests of the OMC scale's predictive validity before its use to assess genuine differences in cognitive style.},
  type = {Preprint}
}

@article{crescenziFindingTopkNodes2020,
  title = {Finding {{Top}}-k {{Nodes}} for {{Temporal Closeness}} in {{Large Temporal Graphs}}},
  author = {Crescenzi, Pierluigi and Magnien, Cl{\'e}mence and Marino, Andrea},
  year = {2020},
  month = aug,
  volume = {13},
  pages = {211},
  issn = {1999-4893},
  doi = {10.3390/a13090211},
  abstract = {The harmonic closeness centrality measure associates, to each node of a graph, the average of the inverse of its distances from all the other nodes (by assuming that unreachable nodes are at infinite distance). This notion has been adapted to temporal graphs (that is, graphs in which edges can appear and disappear during time) and in this paper we address the question of finding the top-k nodes for this metric. Computing the temporal closeness for one node can be done in O(m) time, where m is the number of temporal edges. Therefore computing exactly the closeness for all nodes, in order to find the ones with top closeness, would require O(nm) time, where n is the number of nodes. This time complexity is intractable for large temporal graphs. Instead, we show how this measure can be efficiently approximated by using a ``backward'' temporal breadth-first search algorithm and a classical sampling technique. Our experimental results show that the approximation is excellent for nodes with high closeness, allowing us to detect them in practice in a fraction of the time needed for computing the exact closeness of all nodes. We validate our approach with an extensive set of experiments.},
  file = {/home/aleksandar/Zotero/storage/9F4GKN3X/Crescenzi et al. - 2020 - Finding Top-k Nodes for Temporal Closeness in Larg.pdf},
  journal = {Algorithms},
  language = {en},
  number = {9}
}

@article{cresciDecadeSocialBot2020,
  title = {A {{Decade}} of {{Social Bot Detection}}},
  author = {Cresci, Stefano},
  year = {2020},
  month = jul,
  abstract = {On the morning of November 9th 2016, the world woke up to the shocking outcome of the US Presidential elections: Donald Trump was the 45th President of the United States of America. An unexpected event that still has tremendous consequences all over the world. Today, we know that a minority of social bots, automated social media accounts mimicking humans, played a central role in spreading divisive messages and disinformation, possibly contributing to Trump's victory. In the aftermath of the 2016 US elections, the world started to realize the gravity of widespread deception in social media. Following Trump's exploit, we witnessed to the emergence of a strident dissonance between the multitude of efforts for detecting and removing bots, and the increasing effects that these malicious actors seem to have on our societies. This paradox opens a burning question: What strategies should we enforce in order to stop this social bot pandemic? In these times, during the run-up to the 2020 US elections, the question appears as more crucial than ever. What stroke social, political and economic analysts after 2016, deception and automation, has been however a matter of study for computer scientists since at least 2010. In this work, we briefly survey the first decade of research in social bot detection. Via a longitudinal analysis, we discuss the main trends of research in the fight against bots, the major results that were achieved, and the factors that make this never-ending battle so challenging. Capitalizing on lessons learned from our extensive analysis, we suggest possible innovations that could give us the upper hand against deception and manipulation. Studying a decade of endeavours at social bot detection can also inform strategies for detecting and mitigating the effects of other, more recent, forms of online deception, such as strategic information operations and political trolls.},
  archivePrefix = {arXiv},
  eprint = {2007.03604},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Dropbox/Zotero/Cresci/Cresci_2020_A Decade of Social Bot Detection.pdf;/home/aleksandar/Zotero/storage/EA33TT9X/2007.html},
  journal = {arXiv:2007.03604 [cs]},
  primaryClass = {cs}
}

@book{crickDefencePolitics2011,
  title = {In Defence of Politics},
  author = {Crick, Bernard},
  year = {2011},
  annotation = {OCLC: 1043908261},
  isbn = {978-0-8264-8751-3},
  language = {English}
}

@misc{CriticalFluctuationsEarlyWarning,
  title = {Critical {{Fluctuations}} as an {{Early}}-{{Warning Signal}} for {{Sudden Gains}} and {{Losses}} in {{Patients Receiving Psychotherapy}} for {{Mood Disorders}} - {{Merlijn Olthof}}, {{Fred Hasselman}}, {{Guido Strunk}}, {{Marieke}} van {{Rooij}}, {{Benjamin Aas}}, {{Marieke A}}. {{Helmich}}, {{G\"unter Schiepek}}, {{Anna Lichtwarck}}-{{Aschoff}}, 2020},
  file = {/home/aleksandar/Zotero/storage/Z4TR7L7L/2167702619865969.html},
  howpublished = {https://journals.sagepub.com/doi/full/10.1177/2167702619865969}
}

@article{crokidakisModelingEvolutionDrinking2020,
  title = {Modeling the Evolution of Drinking Behavior: {{A Statistical Physics}} Perspective},
  shorttitle = {Modeling the Evolution of Drinking Behavior},
  author = {Crokidakis, Nuno and Sigaud, Lucas},
  year = {2020},
  month = aug,
  abstract = {In this work we study a simple compartmental model for drinking behavior evolution. The population is divided in 3 compartments regarding their alcohol consumption, namely Susceptible individuals S (nonconsumers), Moderated drinkers M and Risk drinkers R. The transitions among those states are rules by probabilities. Despite the simplicity of the model, we observed the occurrence of two distinct nonequilibrium phase transitions to absorbing states. One of these states is composed only by Susceptible individuals S, with no drinkers (\$M=R=0\$). On the other hand, the other absorbing state is composed only by Risk drinkers R (\$S=M=0\$). Between these two steady states, we have the coexistence of the three subpopulations S, M and R. Comparison with abusive alcohol consumption data for Brazil shows a good agreement between the model's results and the database.},
  archivePrefix = {arXiv},
  eprint = {2008.10692},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Crokidakis_Sigaud/Crokidakis_Sigaud_2020_Modeling the evolution of drinking behavior.pdf;/home/aleksandar/Zotero/storage/AD82ZTLR/2008.html},
  journal = {arXiv:2008.10692 [cond-mat, physics:physics]},
  primaryClass = {cond-mat, physics:physics}
}

@techreport{cronertDemocracyStateCapacity2020,
  title = {Democracy, {{State Capacity}}, and {{COVID}}-19 {{Related School Closures}}},
  author = {Cronert, Axel},
  year = {2020},
  month = mar,
  institution = {{Politics and International Relations}},
  doi = {10.33774/apsa-2020-jf671},
  abstract = {This paper provides a preliminary analysis of the institutional determinants of the timing of COVID-19 related school closures, focusing on the role of democracy and administrative state capacity. To this end, the paper reports a set of survival analyses of school closures around the world in February and March of 2020. Relying foremost on an analysis of the number of days to school closure after the day of the 10th confirmed case of COVID-19 in 134 countries, the paper finds that other things being equal, democratic countries tended to implement school closures more quickly than those with a more authoritarian regime, while countries with higher government effectiveness tended to take longer than those with less effective state apparatuses. Similar, yet\textemdash with respect to democracy\textemdash less substantial results are retrieved in a supplementary analysis of 158 countries, where the starting point is instead the day of China's first COVID-19 related school closure. Note that these results are preliminary and that the paper is not intended to evaluate, and does not say anything about, the appropriateness or effectiveness of any particular school closure strategy.},
  file = {/home/aleksandar/Dropbox/Zotero/Cronert/Cronert_2020_Democracy, State Capacity, and COVID-19 Related School Closures.pdf},
  language = {en},
  type = {Preprint}
}

@article{crucittiCentralityMeasuresSpatial2006,
  title = {Centrality Measures in Spatial Networks of Urban Streets},
  author = {Crucitti, Paolo and Latora, Vito and Porta, Sergio},
  year = {2006},
  month = mar,
  volume = {73},
  pages = {036125},
  issn = {1539-3755, 1550-2376},
  doi = {10.1103/PhysRevE.73.036125},
  journal = {Physical Review E},
  language = {en},
  number = {3}
}

@article{csardiIgraphSoftwarePackage2006,
  ids = {csardiIgraphSoftwarePackage2006a},
  title = {The Igraph Software Package for Complex Network Research},
  author = {Csardi, Gabor and Nepusz, Tamas},
  year = {2006},
  volume = {Complex Systems},
  pages = {1695},
  journal = {InterJournal}
}

@techreport{curryMoralMoleculesMorality2020,
  title = {Moral {{Molecules}}: {{Morality}} as a Combinatorial System},
  shorttitle = {Moral {{Molecules}}},
  author = {Curry, Oliver Scott and Alfano, Mark and Brandt, Mark John and Pelican, Christine},
  year = {2020},
  month = jun,
  institution = {{Open Science Framework}},
  doi = {10.31219/osf.io/xnstk},
  abstract = {Is morality a combinatorial system in which a small number of simple moral `elements' combine to form a large number of complex moral `molecules'? According to the theory of morality-as-cooperation, morality is a collection of biological and cultural solutions to the problems of cooperation recurrent in human social life. As evolutionary game theory has shown, there are many types of cooperation; hence, the theory explains many types of morality, including: family values, group loyalty, reciprocity, heroism, deference, fairness and property rights. As with any set of discrete items, these seven `elements' can, in principle, be combined in multiple ways. But are they in practice? In this paper, we show that they are. For each combination of two elements, we hypothesise candidate moral molecules; and we successfully locate examples of them in the professional and popular literature. These molecules include: fraternity, blood revenge, family pride, filial piety, gavelkind, primogeniture, friendship, patriotism, tribute, diplomacy, common ownership, honour, confession, turn taking, restitution, modesty, mercy, munificence, arbitration, mendicancy, and queuing. Thus morality \textendash{} like many other physical, biological, psychological and cultural systems \textendash{} is indeed a combinatorial system. And morality-as-cooperation provides a principled and systematic taxonomy that has the potential to explain all moral ideas, possible and actual. Pursuing the many implications of this theory will help to place the study of morality on a more secure scientific footing.},
  file = {/home/aleksandar/Dropbox/Zotero/Curry et al/Curry et al_2020_Moral Molecules.pdf},
  language = {en},
  type = {Preprint}
}

@article{curtisIdentificationReligionCrossNational,
  title = {Identification with {{Religion}}: {{Cross}}-{{National Evidence}} from a {{Social Psychological Perspective}}},
  shorttitle = {Identification with {{Religion}}},
  author = {Curtis, K. Amber and Olson, Laura R.},
  volume = {n/a},
  issn = {1468-5906},
  doi = {10.1111/jssr.12634},
  abstract = {Why is religion a more central social identity for some people than for others? Previous studies focus on explaining individual differences in religious affiliation and religiosity, often using the term ``identification'' in reference to these concepts. Yet, few scholars operationalize\textemdash let alone attempt to explain\textemdash identification with religion in social psychological terms, i.e., as a construct that captures the subjective psychological centrality of one's religious identity. After underscoring the benefits of exploring religious identification using cross-national data, we employ an original data set composed of nationally representative surveys in three European countries to model religious identification in two ways: importance (independent strength of attachment) and prominence (prioritization of one's religious identity relative to the others one holds). We document substantial variation in the degree to which individuals define themselves on the basis of their religious identity. We then test predictions drawn from existing theories to model these two measures. Our results extend current understandings of what shapes psychological attachment to religion and raise new questions for future theorization and analysis.},
  copyright = {\textcopyright{} 2019 The Society for the Scientific Study of Religion},
  file = {/home/aleksandar/Zotero/storage/DAILD5T2/jssr.html},
  journal = {Journal for the Scientific Study of Religion},
  language = {en},
  number = {n/a}
}

@article{cutelloOptimizationAlgorithmsDetection2020,
  title = {Optimization {{Algorithms}} for {{Detection}} of {{Social Interactions}}},
  author = {Cutello, Vincenzo and Fargetta, Georgia and Pavone, Mario and Scollo, Rocco A.},
  year = {2020},
  month = jun,
  volume = {13},
  pages = {139},
  issn = {1999-4893},
  doi = {10.3390/a13060139},
  abstract = {Community detection is one of the most challenging and interesting problems in many research areas. Being able to detect highly linked communities in a network can lead to many benefits, such as understanding relationships between entities or interactions between biological genes, for instance. Two different immunological algorithms have been designed for this problem, called OPT-IA and HYBRID-IA, respectively. The main difference between the two algorithms is the search strategy and related immunological operators developed: the first carries out a random search together with purely stochastic operators; the last one is instead based on a deterministic Local Search that tries to refine and improve the current solutions discovered. The robustness of OPT-IA and HYBRID-IA has been assessed on several real social networks. These same networks have also been considered for comparing both algorithms with other seven different metaheuristics and the well-known greedy optimization LOUVAIN algorithm. The experimental analysis conducted proves that OPT-IA and HYBRID-IA are reliable optimization methods for community detection, outperforming all compared algorithms.},
  file = {/home/aleksandar/Documents/Zotero/Cutello et al/Cutello et al_2020_Optimization Algorithms for Detection of Social Interactions.pdf},
  journal = {Algorithms},
  keywords = {DONE},
  language = {en},
  number = {6}
}

@inproceedings{dabasInvestigatingLargeScaleGraphs2021,
  title = {Investigating {{Large}}-{{Scale Graphs}} for {{Community Detection}}},
  booktitle = {Innovations in {{Computational Intelligence}} and {{Computer Vision}}},
  author = {Dabas, Chetna and Nigam, Gaurav Kumar and Nagar, Himanshu},
  editor = {Sharma, Manoj Kumar and Dhaka, Vijaypal Singh and Perumal, Thinagaran and Dey, Nilanjan and Tavares, Jo{\~a}o Manuel R. S.},
  year = {2021},
  pages = {122--129},
  publisher = {{Springer}},
  address = {{Singapore}},
  doi = {10.1007/978-981-15-6067-5_15},
  abstract = {This research paper performs comparison and performance evaluation of large-scale graph techniques such as Walk Trap, Edge Betweenness, Random Walk Fast Greedy, Infomap and Leading Eigen Vector. The performance metrics like Modularity, Normalized Mutual Information, Adjusted Rand Index and Rand Index are used to investigate the performance. The datasets utilized are Yeast Protein Network, US Airport Network, Macaque Network, UK Faculty Network and Enron Email Network. The results are presented in the form of figures and tables.},
  isbn = {9789811560675},
  language = {en},
  series = {Advances in {{Intelligent Systems}} and {{Computing}}}
}

@article{dabkowskiFindingGloballyOptimal2020,
  title = {Finding Globally Optimal Macrostructure in Multiple Relation, Mixed-Mode Social Networks},
  author = {Dabkowski, Matthew F and Fan, Neng and Breiger, Ronald},
  year = {2020},
  month = sep,
  volume = {13},
  pages = {2059799120961693},
  publisher = {{SAGE Publications Ltd}},
  issn = {2059-7991},
  doi = {10.1177/2059799120961693},
  abstract = {From the outset, computational sociologists have stressed leveraging multiple relations when blockmodeling social networks. Despite this emphasis, the majority of published research over the past 40?years has focused on solving blockmodels for a single relation. When multiple relations exist, a reductionist approach is often employed, where the relations are stacked or aggregated into a single matrix, allowing the researcher to apply single relation, often heuristic, blockmodeling techniques. Accordingly, in this article, we develop an exact procedure for the exploratory blockmodeling of multiple relation, mixed-mode networks. In particular, given (a) N1 actors, (b) N2 events, (c) an (N1?N1) binary one-mode network depicting the ties between actors, and (d) an (N1?N2) binary two-mode network representing the ties between actors and events, we use integer programming to find globally optimal (P1?P1|P1?P2) image matrices and partitions, where P1 and P2 represent the number of actor and event positions, respectively. Given the problem?s computational complexity, we also develop an algorithm to generate a minimal set of non-isomorphic image matrices, as well as a complementary, easily accessible heuristic using the network analysis software Pajek. We illustrate these concepts using a simple, hypothetical example, and we apply our techniques to a terrorist network.},
  file = {/home/aleksandar/Documents/Zotero/Dabkowski et al/Dabkowski et al_2020_Finding globally optimal macrostructure in multiple relation, mixed-mode social.pdf},
  journal = {Methodological Innovations},
  number = {3}
}

@techreport{dablanderAnticipatingCriticalTransitions2020,
  title = {Anticipating {{Critical Transitions}} in {{Psychological Systems}} Using {{Early Warning Signals}}: {{Theoretical}} and {{Practical Considerations}}},
  shorttitle = {Anticipating {{Critical Transitions}} in {{Psychological Systems}} Using {{Early Warning Signals}}},
  author = {Dablander, Fabian and Pichler, Anton and Cika, Arta and Bacilieri, Andrea},
  year = {2020},
  month = oct,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/5wc28},
  abstract = {Many real-world systems can exhibit sudden shifts from one stable state to another, and the theory of dynamical systems points to the existence of generic early warning signals that precede such shifts. Recently, psychologists have begun to conceptualize mental disorders such as depression as an alternative stable state, and suggested that early warning signals based on the phenomenon of critical slowing down might be useful for predicting sudden transitions into depression or other psychiatric disorders. Harnessing the potential of early warning signals requires us to understand their limitations as well as the factors influencing their performance in practice. In this paper, we (a) review limitations of early warning signals based on critical slowing down to better understand when they can and cannot occur, and (b) study the conditions under which early warning signals may anticipate critical transitions in online-monitoring settings by simulating from a bistable dynamical system, varying crucial features such as sampling frequency, noise intensity, and speed of approaching the tipping point. We find that, in sharp contrast to their reputation of being generic or model-agnostic, whether early warning signals occur or not strongly depends on the specifics of the system. We also find that they are very sensitive to noise, potentially limiting their utility in practical applications. We discuss the implications of our findings and provide suggestions and recommendations for future research.},
  file = {/home/aleksandar/Dropbox/Zotero/Dablander et al/Dablander et al_2020_Anticipating Critical Transitions in Psychological Systems using Early Warning.pdf},
  keywords = {Clinical Psychology,Computational Modeling,Critical Slowing Down,Dynamical Systems,Early Warning Signals,Psychiatry,Quantitative Methods,Resilience,Social and Behavioral Sciences,Statistical Methods,Tipping Points}
}

@article{dablanderDefaultBayesFactors2020,
  title = {Default {{Bayes Factors}} for {{Testing}} the ({{In}})Equality of {{Several Population Variances}}},
  author = {Dablander, Fabian and van den Bergh, Don and Ly, Alexander and Wagenmakers, Eric-Jan},
  year = {2020},
  month = mar,
  abstract = {Testing the (in)equality of variances is an important problem in many statistical applications. We develop default Bayes factor tests to assess the (in)equality of two or more population variances, as well as a test for whether the population variance equals a specific value. The resulting test can be used to check assumptions for commonly used procedures such as the t-test or ANOVA, or test substantive hypotheses concerning variances directly. We further extend the Bayes factor to allow H0 to have a null-region. Researchers may have directed hypotheses such as {$\sigma$}12 {$>$} {$\sigma$}22, or want to combine hypotheses about equality with hypotheses about inequality, for example {$\sigma$}12 = {$\sigma$}22 {$>$} ({$\sigma$}32, {$\sigma$}42). We generalize our Bayes factor to accommodate such hypotheses for K {$>$} 2 groups. We show that our Bayes factor fulfills a number of desiderata, provide practical examples illustrating the method, and compare it to a recently proposed fractional Bayes factor procedure by B\textasciidieresis oing-Messing and Mulder (2018). Our procedure is implemented in the R package bfvartest.},
  archivePrefix = {arXiv},
  eprint = {2003.06278},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Dropbox/Zotero/Dablander et al/Dablander et al_2020_Default Bayes Factors for Testing the (In)equality of Several Population.pdf},
  journal = {arXiv:2003.06278 [stat]},
  language = {en},
  primaryClass = {stat}
}

@misc{dablanderLoveAffairsLinear2019,
  title = {Love Affairs and Linear Differential Equations},
  author = {Dablander, Fabian},
  year = {2019},
  month = aug,
  abstract = {Differential equations are a powerful tool for modeling how systems change over time, but they can be a little hard to get into. Love, on the other hand, is humanity's perennial topic; some even claim it is all you need. In this blog post \textemdash{} inspired by Strogatz (1988, 2015) \textemdash{} I will introduce linear differential equations as a means to study the types of love affairs two people might find themselves in. Do opposites attract? What happens to a relationship if lovers are out of touch with their own feelings? We will answer these and other questions using two coupled linear differential equations. On our journey, we will use graphical as well as mathematical methods to classify the types of relationships this modeling framework can accommodate. In a follow-up blog post, we will also play around with non-linear terms and add a third wheel to the mix, which can lead to chaos \textemdash{} in the technical sense of the term, of course. Excited? Then let's get started! Introducing Romeo A lovestruck Romeo sang the streets of serenade Laying everybody low with a love song that he made Finds a streetlight, steps out of the shade Says something like, "You and me, babe, how about it?" Romeo is quite the emotional type. Let \$R(t)\$ denote his feelings at time point \$t\$. Following common practice, we will usually write \$R\$ instead of \$R(t)\$, making the time dependence implicit. The process which describes how Romeo's feelings change is rather simple: it depends only on Romeo's current feelings. We write: which is a linear differential equation. Note that this implicitly encodes how Romeo's feelings change over time, since when we know \$R\$ at time point \$t\$, we can compute the direction and speed with which \$R\$ will change \textemdash{} the derivative denotes velocity. Our goal, however, is to find an explicit, closed-form expression for Romeo's feelings at time point \$t\$. In this particular case, we can do this analytically: A differential equation describes how something changes; to kickstart the process, we need an initial condition \$R\_0\$. This allows us to find the constant of integration \$C\$. In particular, assume that \$R = R\_0\$ at \$t = 0\$, which leads to: such that: The left two panels of the figure below visualize how Romeo's feelings change over time for \$a {$>$} 0\$ with initial condition \$R\_0 = 1\$ (top) or \$R\_0 = -1\$ (bottom). The right two panels show how his feelings change for \$a {$<$} 0\$ with \$R\_0 = 100\$ (top) or \$R\_0 = -100\$ (bottom). We conclude: Romeo is a simple guy, albeit with an emotion regulation problem. When the object of his affection is such that \$a {$>$} 0\$, his feelings will either grow exponentially towards mad love if he starts out with a positive first impression (\$R\_0 {$>$} 0\$), or grow exponentially towards mad hatred if he starts out with a negative first impression (\$R\_0 {$<$} 0\$). On the other hand, if \$a {$<$} 0\$, then regardless of his initial feelings, they will decay exponentially towards indifference. For \$R\_0 = 0\$, Romeo's feelings never change. For any other initial condition, we have uhindered, exponential growth when \$a {$>$} 0\$; it never stops. For any other initial condition and \$a {$<$} 0\$, we crash down to zero very rapidly. Thus \$R = 0\$ is a fixed point in both cases, which is stable for \$a {$<$} 0\$ but becomes unstable if \$a {$>$} 0\$. We can visualize this in phase space on a line. The phase space is filled with all possible trajectories because each point can serve as the initial condition. In the next section, a wonderful new episode in Romeo's life begins: he meets Juliet. Introducing Juliet Juliet says, "Hey, it's Romeo, you nearly gave me a heart attack" He's underneath the window, she's singing, "Hey, la, my boyfriend's back You shouldn't come around here singing up at people like that Anyway, what you gonna do about it?" Life becomes more complicated for Romeo now that Juliet is in his life. It is their first real relationship, and they have much to learn. We start simple. Let \$J\$ denote Juliet's feelings for Romeo, and let \$R\$ denote Romeo's feelings for Juliet. We can extend our single linear differential equation from above to a system of two linear differential equations: Using the results from above, the solutions to the two differential equations are: where \$R(t)\$ and \$J(t)\$ give the trajectories of love for Romeo and Juliet, respectively, and \$J\_0\$ is Juliet's initial feeling towards Romeo at \$t = 0\$. In contrast to the one-dimensional phase diagram from above, we now have a two-dimensional picture which is known as a vector field.},
  howpublished = {https://fabiandablander.com/r/Linear-Love.html},
  journal = {Fabian Dablander}
}

@article{dablanderNodeCentralityMeasuresa,
  ids = {dablanderNodeCentralityMeasures},
  title = {Node {{Centrality Measures}} Are a Poor Substitute for {{Causal Inference}}},
  author = {Dablander, Fabian and Hinne, Max},
  doi = {10.31234/osf.io/nue4z},
  abstract = {Network models have become a valuable tool in making sense of a diverse range of social, biological, and information systems. These models marry graph and probability theory to visualize, understand, and interpret variables and their relations as nodes and edges in a graph. Many applications of network models rely on undirected graphs in which the absence of an edge between two nodes encodes conditional independence between the corresponding variables. To gauge the importance of nodes in such a network, various node centrality measures have become widely used, especially in psychology and neuroscience. It is intuitive to interpret nodes with high centrality measures as being important in a causal sense. Here, using the causal framework based on directed acyclic graphs (DAGs), we show that the relation between causal influence and node centrality measures is not straightforward. In particular, the correlation between causal influence and several node centrality measures is weak, except for eigenvector centrality. Our results provide a cautionary tale: if the underlying real-world system can be modeled as a DAG, but researchers interpret nodes with high centrality as causally important, then this may result in sub-optimal interventions.}
}

@article{dacremaMechanicsContentiousPolitics2020,
  ids = {dacremaMechanicsContentiousPolitics},
  title = {The Mechanics of Contentious Politics: An Agent-Based Modeling Approach},
  shorttitle = {The Mechanics of Contentious Politics},
  author = {Dacrema, Eugenio and Benati, Stefano},
  year = {2020},
  month = jul,
  volume = {44},
  pages = {163--198},
  publisher = {{Routledge}},
  issn = {0022-250X},
  doi = {10.1080/0022250X.2020.1753187},
  abstract = {``Contentious politics'' has become the main label to define a wide range of previously separated fields of research encompassing topics such as collective action, radicalization, armed insurgencies, and terrorism. Over the past two decades, scholars have tried to bring these various strands together into a unified field of study. In so doing, they have developed a methodology to isolate and analyze the common social and cognitive mechanisms underlying several diverse historical phenomena such as ``insurgencies,'' ``revolutions,'' ``radicalization,'' or ``terrorism.'' A multidisciplinary approach was adopted open to contributions from diverse fields such as economics, sociology, and psychology. The aim of this paper is to add to the multidisciplinarity of the field of Contentious Politics (CP) and introduce the instruments of Agent-Based Modeling and network game-theory to the study of some fundamental mechanisms analyzed within this literature. In particular, the model presented in this paper describes the dynamics of one process, here defined as ``the radicalization of politics,'' and its main underlying mechanisms. Their mechanics are analyzed in diverse social contexts differentiated by the values of four parameters: the extent of repression, inequality, social tolerance, and interconnectivity. The model can be used to explain the basic dynamics underlying different phenomena such as the development of radicalization, populism, and popular rebellions. In the final part, different societies characterized by diverse values of the aforementioned four parameters are tested through Python simulations, thereby offering an overview of the different outcomes that the mechanics of our model can shape according to the contexts in which they operate.},
  annotation = {\_eprint: https://doi.org/10.1080/0022250X.2020.1753187},
  journal = {The Journal of Mathematical Sociology},
  number = {3}
}

@book{dahl2008polyarchy,
  title = {Polyarchy: {{Participation}} and Opposition},
  author = {Dahl, R.A.},
  year = {2008},
  publisher = {{Yale University Press}},
  isbn = {978-0-300-15357-6}
}

@article{dahlbergSocialIdentificationIngroup,
  title = {Social Identification, in-Group Integration and Voter Turnout in Three Parliamentary Elections: {{An}} Analysis of the {{Swedish S\'ami}} Electorate},
  author = {Dahlberg, Stefan and M{\"o}rkenstam, Ulf},
  doi = {10.1016/j.electstud.2019.03.012},
  journal = {Electoral Studies}
}

@article{dahlbergSocialIdentificationIngroupa,
  title = {Social Identification, in-Group Integration and Voter Turnout in Three Parliamentary Elections: {{An}} Analysis of the {{Swedish S\'ami}} Electorate},
  author = {Dahlberg, Stefan and M{\"o}rkenstam, Ulf},
  doi = {10.1016/j.electstud.2019.03.012},
  journal = {Electoral Studies}
}

@article{daiGeneralizedEntropyPlane,
  title = {Generalized Entropy Plane Based on Permutation Entropy and Distribution Entropy Analysis for Complex Time Series},
  author = {Dai, Yimei and He, Jiayi and Wu, Yue and Chen, Shijian and Shang, Pengjian},
  doi = {S0378437119300172},
  abstract = {{$<$}p{$>$}Publication date: Available online 14 January 2019{$<$}/p{$><$}p{$>$}\textbf{Source:} Physica A: Statistical Mechanics and its Applications{$<$}/p{$><$}p{$>$}Author(s): Yimei Dai, Jiayi He, Yue Wu, Shijian Chen, Pengjian Shang{$<$}/p{$><$}h5{$>$}Abstract{$<$}/h5{$><$}div{$><$}p{$>$}Entropy is an accessible way to work as a measure of the irregularity and the uncertainty between the predicting knowledge and the given time series. Statistical complexity measure (SCM) combining Shannon entropy and the extensive Jensen\textendash Shannon divergence provides important additional information regarding the peculiarities of the underlying probability distribution, not already detected by the entropy. In this paper, we extend the traditional complexity-entropy causality plane, which applies the diagram of SCM versus normalized Shannon entropy, to two generalized complexity-entropy plane based on Permutation entropy (PE) and Permuted distribution entropy (PEDisEn). Moreover, as the important extension of the Shannon entropy, the Tsallis entropy and R\'enyi entropy are used to construct the plane. We discuss the parameter selection for the PE plane and PEDisEn plane respectively. Outlier detection is recently a heated point focusing on discovering patterns that occur infrequently in the time series in data mining. However, there exists few entropy plane based methods in outlier detection. We apply the proposed procedure to the real world data for outlier detection. It turns out that the generalized entropy plane is robust to the type of original series and is efficient for detecting outliers.{$<$}/p{$><$}/div{$>$}},
  journal = {Physica A: Statistical Mechanics and its Applications}
}

@article{daiGeneralizedEntropyPlanea,
  title = {Generalized Entropy Plane Based on Permutation Entropy and Distribution Entropy Analysis for Complex Time Series},
  author = {Dai, Yimei and He, Jiayi and Wu, Yue and Chen, Shijian and Shang, Pengjian},
  doi = {S0378437119300172},
  abstract = {{$<$}p{$>$}Publication date: Available online 14 January 2019{$<$}/p{$><$}p{$>$}\textbf{Source:} Physica A: Statistical Mechanics and its Applications{$<$}/p{$><$}p{$>$}Author(s): Yimei Dai, Jiayi He, Yue Wu, Shijian Chen, Pengjian Shang{$<$}/p{$><$}h5{$>$}Abstract{$<$}/h5{$><$}div{$><$}p{$>$}Entropy is an accessible way to work as a measure of the irregularity and the uncertainty between the predicting knowledge and the given time series. Statistical complexity measure (SCM) combining Shannon entropy and the extensive Jensen\textendash Shannon divergence provides important additional information regarding the peculiarities of the underlying probability distribution, not already detected by the entropy. In this paper, we extend the traditional complexity-entropy causality plane, which applies the diagram of SCM versus normalized Shannon entropy, to two generalized complexity-entropy plane based on Permutation entropy (PE) and Permuted distribution entropy (PEDisEn). Moreover, as the important extension of the Shannon entropy, the Tsallis entropy and R\'enyi entropy are used to construct the plane. We discuss the parameter selection for the PE plane and PEDisEn plane respectively. Outlier detection is recently a heated point focusing on discovering patterns that occur infrequently in the time series in data mining. However, there exists few entropy plane based methods in outlier detection. We apply the proposed procedure to the real world data for outlier detection. It turns out that the generalized entropy plane is robust to the type of original series and is efficient for detecting outliers.{$<$}/p{$><$}/div{$>$}},
  journal = {Physica A: Statistical Mechanics and its Applications}
}

@article{dalegeAttitudinalEntropyAE2018,
  title = {The {{Attitudinal Entropy}} ({{AE}}) {{Framework}}: {{Clarifications}}, {{Extensions}}, and {{Future Directions}}},
  shorttitle = {The {{Attitudinal Entropy}} ({{AE}}) {{Framework}}},
  author = {Dalege, Jonas and Borsboom, Denny and {van Harreveld}, Frenk and Lunansky, Gabriela and {van der Maas}, Han L. J.},
  year = {2018},
  month = oct,
  volume = {29},
  pages = {218--228},
  issn = {1047-840X, 1532-7965},
  doi = {10.1080/1047840X.2018.1542235},
  journal = {Psychological Inquiry},
  language = {en},
  number = {4}
}

@article{dalegeFormalizedAccountAttitudes2016,
  title = {Toward a Formalized Account of Attitudes: {{The Causal Attitude Network}} ({{CAN}}) Model.},
  shorttitle = {Toward a Formalized Account of Attitudes},
  author = {Dalege, Jonas and Borsboom, Denny and {van Harreveld}, Frenk and {van den Berg}, Helma and Conner, Mark and {van der Maas}, Han L. J.},
  year = {2016},
  volume = {123},
  pages = {2--22},
  issn = {1939-1471, 0033-295X},
  doi = {10.1037/a0039802},
  journal = {Psychological Review},
  language = {en},
  number = {1}
}

@article{dalegeNetworkAnalysisAttitudes2017,
  title = {Network {{Analysis}} on {{Attitudes}}: {{A Brief Tutorial}}},
  shorttitle = {Network {{Analysis}} on {{Attitudes}}},
  author = {Dalege, Jonas and Borsboom, Denny and {van Harreveld}, Frenk and {van der Maas}, Han L. J.},
  year = {2017},
  month = jul,
  volume = {8},
  pages = {528--537},
  issn = {1948-5506, 1948-5514},
  doi = {10.1177/1948550617709827},
  journal = {Social Psychological and Personality Science},
  language = {en},
  number = {5}
}

@article{dalegeNetworkPerspectiveAttitude2017,
  title = {A {{Network Perspective}} on {{Attitude Strength}}: {{Testing}} the {{Connectivity Hypothesis}}},
  shorttitle = {A {{Network Perspective}} on {{Attitude Strength}}},
  author = {Dalege, Jonas and Borsboom, Denny and {van Harreveld}, Frenk and {van der Maas}, Han L. J.},
  year = {2017},
  month = apr,
  abstract = {Attitude strength is a key characteristic of attitudes. Strong attitudes are durable and impactful, while weak attitudes are fluctuating and inconsequential. Recently, the Causal Attitude Network (CAN) model was proposed as a comprehensive measurement model of attitudes, which conceptualizes attitudes as networks of causally connected evaluative reactions (i.e., beliefs, feelings, and behavior toward an attitude object). Here, we test the central postulate of the CAN model that highly connected attitude networks correspond to strong attitudes. We use data from the American National Election Studies 1980-2012 on attitudes toward presidential candidates (total n = 18,795). We first show that political interest predicts connectivity of attitude networks toward presidential candidates. Second, we show that connectivity is strongly related to two defining features of strong attitudes - stability of the attitude and the attitude's impact on behavior. We conclude that network theory provides a promising framework to advance the understanding of attitude strength.},
  archivePrefix = {arXiv},
  eprint = {1705.00193},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Zotero/storage/ZLUFQA66/1705.html},
  journal = {arXiv:1705.00193 [physics]},
  primaryClass = {physics}
}

@article{dalegeNetworkPerspectiveAttitude2019,
  title = {A {{Network Perspective}} on {{Attitude Strength}}: {{Testing}} the {{Connectivity Hypothesis}}},
  shorttitle = {A {{Network Perspective}} on {{Attitude Strength}}},
  author = {Dalege, Jonas and Borsboom, Denny and {van Harreveld}, Frenk and {van der Maas}, Han L. J.},
  year = {2019},
  month = aug,
  volume = {10},
  pages = {746--756},
  issn = {1948-5506, 1948-5514},
  doi = {10.1177/1948550618781062},
  abstract = {Attitude strength is a key characteristic of attitudes. Strong attitudes are durable and impactful, while weak attitudes are fluctuating and inconsequential. Recently, the causal attitude network (CAN) model was proposed as a comprehensive measurement model of attitudes, which conceptualizes attitudes as networks of causally connected evaluative reactions (i.e., beliefs, feelings, and behavior toward an attitude object). Here, we test the central postulate of the CAN model that highly connected attitude networks correspond to strong attitudes. We use data from the American National Election Studies 1980\textendash 2012 on attitudes toward presidential candidates ( N = 18,795). We first show that political interest predicts connectivity of attitude networks toward presidential candidates. Second, we show that connectivity is strongly related to two defining features of strong attitudes\textemdash stability of the attitude and the attitude's impact on behavior. We conclude that network theory provides a promising framework to advance the understanding of attitude strength.},
  journal = {Social Psychological and Personality Science},
  language = {en},
  number = {6}
}

@article{dalegeNetworkStructureExplains2017,
  title = {Network {{Structure Explains}} the {{Impact}} of {{Attitudes}} on {{Voting Decisions}}},
  author = {Dalege, Jonas and Borsboom, Denny and {van Harreveld}, Frenk and Waldorp, Lourens J. and {van der Maas}, Han L. J.},
  year = {2017},
  month = dec,
  volume = {7},
  pages = {4909},
  issn = {2045-2322},
  doi = {10.1038/s41598-017-05048-y},
  journal = {Scientific Reports},
  language = {en},
  number = {1}
}

@article{dallavalleBayesianAnalysisImmigration2019,
  ids = {valleBayesianAnalysisImmigration,valleBayesianAnalysisImmigrationa},
  title = {Bayesian Analysis of Immigration in {{Europe}} with Generalized Logistic Regression},
  author = {Dalla Valle, Luciana and Leisen, Fabrizio and Rossini, Luca and Zhu, Weixuan},
  year = {2019},
  month = jul,
  pages = {1--15},
  issn = {0266-4763, 1360-0532},
  doi = {10.1080/02664763.2019.1642310},
  journal = {Journal of Applied Statistics},
  language = {en}
}

@article{daltonPublicImagesPolitical2005,
  title = {Public Images of Political Parties: {{A}} Necessary Evil?},
  shorttitle = {Public Images of Political Parties},
  author = {Dalton, Russell J. and Weldon, Steven A.},
  year = {2005},
  month = nov,
  volume = {28},
  pages = {931--951},
  issn = {0140-2382, 1743-9655},
  doi = {10.1080/01402380500310527},
  file = {/home/aleksandar/Documents/Zotero/Dalton_Weldon/Dalton_Weldon_2005_Public images of political parties.pdf},
  journal = {West European Politics},
  language = {en},
  number = {5}
}

@article{danchevNeitherGlobalLocal2018,
  title = {Neither Global nor Local: {{Heterogeneous}} Connectivity in Spatial Network Structures of World Migration},
  shorttitle = {Neither Global nor Local},
  author = {Danchev, Valentin and Porter, Mason A.},
  year = {2018},
  month = may,
  volume = {53},
  pages = {4--19},
  issn = {03788733},
  doi =