text:
home/aleksandar/Zotero/storage/HFHZWJ8R/Shore et al. - Network Centralization and Collective Intelligence.pdf},
  language = {en}
}

@inproceedings{shultsArtificialSocialEthics2020,
  title = {Artificial Social Ethics: Simulating Culture, Conflict, and Cooperation},
  shorttitle = {Artificial Social Ethics},
  booktitle = {Proceedings of the 2020 {{Spring Simulation Conference}}},
  author = {Shults, F. LeRon and Wildman, Wesley J.},
  year = {2020},
  month = may,
  pages = {1--11},
  publisher = {{Society for Computer Simulation International}},
  address = {{Fairfax, Virginia}},
  abstract = {In recent years advances in computational modeling and social simulation technologies have enabled scientists to identify some of the conditions under which - and the mechanisms by which - conflict and cooperation within and across human cultures are likely to emerge. There are significant ethical concerns surrounding the increased capacity and growing use of such computer tools to guide public policy discussions. The purpose of this paper is to propose and promote an "artificial social ethics" approach to addressing these concerns and illustrate its application in relation to three agent-based models implemented within the Artificial Society Analytics Platform. We conclude with a brief discussion of next steps in future work, and an invitation to join the ethical conversation around the development and deployment of artificial societies designed to address societal challenges resulting from climate change and other crises.},
  file = {/home/aleksandar/Dropbox/Zotero/Shults_Wildman/Shults_Wildman_2020_Artificial social ethics.pdf},
  isbn = {978-1-71381-288-3},
  series = {{{SpringSim}} '20}
}

@article{sibisiGrowthRandomTrees2020,
  title = {Growth of {{Random Trees}} by {{Leaf Attachment}}},
  author = {Sibisi, Nomvelo},
  year = {2020},
  month = oct,
  abstract = {We study the growth of a time-ordered rooted tree by probabilistic attachment of new vertices to leaves. We construct a likelihood function of the leaves based on the connectivity of the tree. We take such connectivity to be induced by the merging of directed ordered paths from leaves to the root. Combining the likelihood with an assigned prior distribution leads to a posterior leaf distribution from which we sample attachment points for new vertices. We present computational examples of such Bayesian tree growth. Although the discussion is generic, the initial motivation for the paper is the concept of a distributed ledger, which may be regarded as a time-ordered random tree that grows by probabilistic leaf attachment.},
  archivePrefix = {arXiv},
  eprint = {2010.05589},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Sibisi/Sibisi_2020_Growth of Random Trees by Leaf Attachment.pdf;/home/aleksandar/Zotero/storage/7IWLH6KX/2010.html},
  journal = {arXiv:2010.05589 [cs, math, stat]},
  primaryClass = {cs, math, stat}
}

@article{siegenfeldIntroductionComplexSystems2020,
  title = {An {{Introduction}} to {{Complex Systems Science}} and Its {{Applications}}},
  author = {Siegenfeld, Alexander F. and {Bar-Yam}, Yaneer},
  year = {2020},
  month = jul,
  volume = {2020},
  pages = {1--16},
  issn = {1076-2787, 1099-0526},
  doi = {10.1155/2020/6105872},
  abstract = {The standard assumptions that underlie many conceptual and quantitative frameworks do not hold for many complex physical, biological, and social systems. Complex systems science clarifies when and why such assumptions fail and provides alternative frameworks for understanding the properties of complex systems. This review introduces some of the basic principles of complex systems science, including complexity profiles, the tradeoff between efficiency and adaptability, the necessity of matching the complexity of systems to that of their environments, multi-scale analysis, and evolutionary processes. Our focus is on the general properties of systems as opposed to the modeling of specific dynamics; rather than provide a comprehensive review, we pedagogically describe a conceptual and analytic approach for understanding and interacting with the complex systems of our world. With the exception of a few footnotes, this paper assumes only a high school mathematical and scientific background, so that it may be accessible to academics in all fields, decision-makers in industry, government, and philanthropy, and anyone who is interested in systems and society.},
  archivePrefix = {arXiv},
  eprint = {1912.05088},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Dropbox/Zotero/Siegenfeld_Bar-Yam/Siegenfeld_Bar-Yam_2020_An Introduction to Complex Systems Science and its Applications.pdf;/home/aleksandar/Zotero/storage/D3CKXZET/1912.html},
  journal = {Complexity},
  keywords = {Physics - Physics and Society}
}

@article{siegenfeldNegativeRepresentationInstability2020,
  title = {Negative Representation and Instability in Democratic Elections},
  author = {Siegenfeld, Alexander F. and {Bar-Yam}, Yaneer},
  year = {2020},
  month = feb,
  volume = {16},
  pages = {186--190},
  publisher = {{Nature Publishing Group}},
  issn = {1745-2481},
  doi = {10.1038/s41567-019-0739-6},
  abstract = {The challenge of understanding the collective behaviours of social systems can benefit from methods and concepts from physics1\textendash 6, not because humans are similar to electrons, but because certain large-scale behaviours can be understood without an understanding of the small-scale details7, in much the same way that sound waves can be understood without an understanding of atoms. Democratic elections are one such behaviour. Over the past few decades, physicists have explored scaling patterns in voting and the dynamics of political opinion formation (for example, see refs. 8\textendash 13). Here, we define the concepts of negative representation, in which a shift in electorate opinions produces a shift in the election outcome in the opposite direction, and electoral instability, in which an arbitrarily small change in electorate opinions can dramatically swing the election outcome, and prove that unstable elections necessarily contain negatively represented opinions. Furthermore, in the presence of low voter turnout, increasing polarization of the electorate can drive elections through a transition from a stable to an unstable regime, analogous to the phase transition by which some materials become ferromagnetic below their critical temperatures. Empirical data suggest that the United States' presidential elections underwent such a phase transition in the 1970s and have since become increasingly unstable.},
  copyright = {2020 The Author(s), under exclusive licence to Springer Nature Limited},
  file = {/home/aleksandar/Dropbox/Zotero/Siegenfeld_Bar-Yam/Siegenfeld_Bar-Yam_2020_Negative representation and instability in democratic elections.pdf;/home/aleksandar/Zotero/storage/WXNN7SYF/Siegenfeld and Bar-Yam - 2020 - Negative representation and instability in democra.pdf;/home/aleksandar/Zotero/storage/6ZJKT73B/s41567-019-0739-6.html},
  journal = {Nature Physics},
  language = {en},
  number = {2}
}

@techreport{sieversHowConsensusbuildingConversation2020,
  title = {How Consensus-Building Conversation Changes Our Minds and Aligns Our Brains},
  author = {Sievers, Beau and Welker, Christopher and Hasson, Uri and Kleinbaum, Adam M. and Wheatley, Thalia},
  year = {2020},
  month = jul,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/562z7},
  abstract = {How do people come to share a way of seeing the world? We show that consensus-building conversation can align future brain activity. We scanned participants' brains as they watched previously unseen movie clips with ambiguous narrative content. Participants then gathered in conversation groups with the goal of coming to a consensus about each movie clip's narrative. Finally, participants were scanned while viewing the clips again, along with novel clips from the same movies. We found that group members' neural activity became more aligned a er conversation, with each group achieving a distinctive pattern of alignment reflecting their unique discussion. This alignment persisted over novel clips, framing new but related information. Finally, we found that participants who were central in their real-world social networks played an outsized role in creating group alignment, both by facilitating conversation and by being more likely to adapt their own brain activity to the group. These results suggest that the e ects of conversation on private thought are a powerful determinant of social influence. We discuss implications for theories of social influence, language, and the mind.},
  file = {/home/aleksandar/Zotero/storage/5DJI9WYT/Sievers et al. - 2020 - How consensus-building conversation changes our mi.pdf},
  language = {en},
  type = {Preprint}
}

@article{siewInvestigatingInfluenceInverse2020,
  title = {Investigating the {{Influence}} of {{Inverse Preferential Attachment}} on {{Network Development}}},
  author = {Siew, Cynthia S. Q. and Vitevitch, Michael S.},
  year = {2020},
  month = sep,
  volume = {22},
  pages = {1029},
  issn = {1099-4300},
  doi = {10.3390/e22091029},
  abstract = {Recent work investigating the development of the phonological lexicon, where edges between words represent phonological similarity, have suggested that phonological network growth may be partly driven by a process that favors the acquisition of new words that are phonologically similar to several existing words in the lexicon. To explore this growth mechanism, we conducted a simulation study to examine the properties of networks grown by inverse preferential attachment, where new nodes added to the network tend to connect to existing nodes with fewer edges. Specifically, we analyzed the network structure and degree distributions of artificial networks generated via either preferential attachment, an inverse variant of preferential attachment, or combinations of both network growth mechanisms. The simulations showed that network growth initially driven by preferential attachment followed by inverse preferential attachment led to densely-connected network structures (i.e., smaller diameters and average shortest path lengths), as well as degree distributions that could be characterized by non-power law distributions, analogous to the features of real-world phonological networks. These results provide converging evidence that inverse preferential attachment may play a role in the development of the phonological lexicon and reflect processing costs associated with a mature lexicon structure.},
  file = {/home/aleksandar/Zotero/storage/YEAQ6RHY/Siew and Vitevitch - 2020 - Investigating the Influence of Inverse Preferentia.pdf},
  journal = {Entropy},
  language = {en},
  number = {9}
}

@article{sikderModellingHeterogeneousOutcomes2020,
  title = {Modelling Heterogeneous Outcomes in Multi-Agent Systems},
  author = {Sikder, Orowa},
  year = {2020},
  month = jul,
  abstract = {A broad set of empirical phenomenon in the study of social, economic and machine behaviour can be modelled as complex systems with averaging dynamics. However many of these models naturally result in consensus or consensus-like outcomes. In reality, empirical phenomenon rarely converge to these and instead are characterized by rich, persistent variation in the agent states. Such heterogeneous outcomes are a natural consequence of a number of models that incorporate external perturbation to the otherwise convex dynamics of the agents. The purpose of this paper is to formalize the notion of heterogeneity and demonstrate which classes of models are able to achieve it as an outcome, and therefore are better suited to modelling important empirical questions. We do so by determining how the topology of (time-varying) interaction networks restrict the space of possible steady-state outcomes for agents, and how this is related to the study of random walks on graphs. We consider a number of intentionally diverse examples to demonstrate how the results can be applied.},
  archivePrefix = {arXiv},
  eprint = {2007.01077},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Zotero/storage/GHNSSY9T/Sikder - 2020 - Modelling heterogeneous outcomes in multi-agent sy.pdf;/home/aleksandar/Zotero/storage/CCH2JAN5/2007.html},
  journal = {arXiv:2007.01077 [physics]},
  primaryClass = {physics}
}

@article{sikkHowUnstableVolatility2005,
  title = {How Unstable? {{Volatility}} and the Genuinely New Parties in {{Eastern Europe}}},
  shorttitle = {How Unstable?},
  author = {Sikk, Allan},
  year = {2005},
  month = may,
  volume = {44},
  pages = {391--412},
  issn = {0304-4130, 1475-6765},
  doi = {10.1111/j.1475-6765.2005.00232.x},
  journal = {European Journal of Political Research},
  language = {en},
  number = {3}
}

@article{silvaKnowledgetransferCharacterizationDynamic,
  title = {On Knowledge-Transfer Characterization in Dynamic Attributed Networks},
  author = {Silva, Thiago H. P. and Laender, Alberto H. F. and {Vaz de Melo}, Pedro O. S.},
  doi = {10.1007/s13278-020-00657-4},
  abstract = {How do social aspects influence knowledge transfer in dynamic attributed networks? We address this issue by characterizing the behavior of the actors and their dynamic interactions based on the strategic positioning in a social structure. For this, we propose a method to characterize the behavior of nodes and their dynamic relationships based on temporal node attributes that capture how knowledge is transferred across a network. In order to assess our method, we apply it to unveil the differences of social relationships in distinct academic social networks and Q\&A communities. We also validate our social definitions considering the importance of the nodes and edges in a social structure by means of network properties, as well as investigate the robustness of our method by stressing it for dealing with the time existence of the nodes in a network and the randomness of attributes associated with them. Moreover, we propose an unsupervised method to measure the academic importance of researchers based on our knowledge-transfer model, which outperforms traditional network metrics and other social-based approaches.},
  journal = {Social Network Analysis and Mining}
}

@article{simkoWhatWeTalk2020,
  title = {What We Talk about When We Talk about Culture: A Multi-Facet Approach},
  shorttitle = {What We Talk about When We Talk about Culture},
  author = {Simko, Christina and Olick, Jeffrey K.},
  year = {2020},
  month = jan,
  issn = {2049-7121},
  doi = {10.1057/s41290-019-00094-7},
  abstract = {Though the cultural turn is well behind us, sociologists of culture have yet to agree upon a clear definition of their subject. Recent efforts in this direction have characterized culture as a meta-concept, identifying and disentangling its constituent parts. Building on the two- and three-dimensional models that have emerged of late, while also taking stock of the longer history of sociology's struggle to reconceptualize culture after Parsons, we argue that culture is best understood via a four-facet model. Culture, we argue, consists of discourse, which is oriented toward meaning, and practice, which is oriented toward action. But both discourse and practice have implicit and explicit dimensions: they consist of underlying generative structures and concrete manifestations. To demonstrate the four-facet model's value for empirical analysis, we use it to address the high-profile methodological debates that have recently roiled the field, pitting ethnography, interviewing, and survey research against one another as the ideal mode of accessing culture writ large. Rather than arbitrating among methods, we show, the four-facet model specifies how each approach contributes to cultural analysis, facilitating conversation in places where disagreements currently appear intractable and underwriting methodological pluralism.},
  journal = {American Journal of Cultural Sociology},
  language = {en}
}

@article{simonShorttermLongtermEffects2018,
  title = {The Short-Term and Long-Term Effects of Institutional Reforms on Party System Nationalization},
  author = {Sim{\'o}n, Pablo and Guinjoan, Marc},
  year = {2018},
  month = sep,
  volume = {16},
  pages = {762--782},
  issn = {1472-4790, 1740-388X},
  doi = {10.1057/s41295-017-0100-3},
  journal = {Comparative European Politics},
  language = {en},
  number = {5}
}

@article{singhCentralityMeasuresTool2020,
  title = {Centrality {{Measures}}: {{A Tool}} to {{Identify Key Actors}} in {{Social Networks}}},
  shorttitle = {Centrality {{Measures}}},
  author = {Singh, Rishi Ranjan},
  year = {2020},
  month = nov,
  abstract = {Experts from several disciplines have been widely using centrality measures for analyzing large as well as complex networks. These measures rank nodes/edges in networks by quantifying a notion of the importance of nodes/edges. Ranking aids in identifying important and crucial actors in networks. In this chapter, we summarize some of the centrality measures that are extensively applied for mining social network data. We also discuss various directions of research related to these measures.},
  archivePrefix = {arXiv},
  eprint = {2011.01627},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Singh/Singh_2020_Centrality Measures.pdf;/home/aleksandar/Zotero/storage/DQQS4EUS/2011.html},
  journal = {arXiv:2011.01627 [cs]},
  keywords = {Computer Science - Social and Information Networks},
  primaryClass = {cs}
}

@inproceedings{singhTippingPointSocial2020,
  title = {The {{Tipping Point In Social Networks}}: {{Investigating}} the {{Mechanism Behind Viral Information Spreading}}},
  shorttitle = {The {{Tipping Point In Social Networks}}},
  booktitle = {2020 {{IEEE Sixth International Conference}} on {{Big Data Computing Service}} and {{Applications}} ({{BigDataService}})},
  author = {Singh, Abhishek and Dharamshi, Niraj and Thimma Govarthanarajan, Preethi and Samale, Premal Dattatray and Eirinaki, Magdalini},
  year = {2020},
  month = aug,
  pages = {54--61},
  doi = {10.1109/BigDataService49289.2020.00016},
  abstract = {A social network represents ties between people. Modeling social interactions to understand the underlying mechanism of herd behavior has been a topic of interest for the past several decades. Our focus in this work is to investigate the phenomenon of extreme, unexpected growth and gain in a social network, and how different diffusion models can be used to identify the "key players" in such a process. In social network analysis, it is a tedious task to find the most influential entities, and how ideas or news spread through the network. A popular method to solve this task is to represent the social network as a graph. In a dynamic or an ever-growing social graph, discovering when an idea starts spreading, and not when it started first, reflects the real nature of how viral ideas catch-on in society. Also, instead of locating the root of an idea, it is crucial to see who propelled the idea to the surface, and triggered the spread. In this paper, we develop a method to identify the set of nodes who are responsible for extraordinary change in graph growth. We propose a methodology that allows us to identify the phenomenon called "tipping point", both in terms of the the role of a node or a community in graph growth, but also with respect to the time frame it occurred. Our framework is employing different influence propagation approaches, to locate critical nodes or communities and indicate their role in the early stages of the sudden graph growth. We demonstrate how the methodology is applied using a real-life social network. We aspire that the proposed framework will help researchers better understand this phenomenon and be able to identify agents responsible for spreading trends, beliefs, opinions, etc. in society.},
  file = {/home/aleksandar/Zotero/storage/KY68BVNG/9179513.html}
}

@article{sistrunkSyntheticDataGeneration2020,
  title = {On Synthetic Data Generation for Anomaly Detection in Complex Social Networks},
  author = {Sistrunk, Andreea and Cedeno, Vanessa and Biswas, Subhodip},
  year = {2020},
  month = oct,
  abstract = {This paper studies the feasibility of synthetic data generation for mission-critical applications. The emphasis is on synthetic data generation for anomalous detection in complex social networks. In particular, the development of a heuristic generative model, capable of creating data for anomalous rare activities in complex social networks is sought. To this end, lessons from social and political literature are applied to prototype a novel implementation of the Agent-based Modeling (ABM) framework, based on simple social interactions between agents, for synthetic data generation in the context of terrorist profile desegregation. The conclusion offers directions for further verification, fine-tuning, and proposes future directions of work for the ABM prototype, as a complex-societal approach to synthetic data generation, by identifying heuristic hyper-parameter tuning methodologies to further ensure the generated data distribution is similar to the true distribution of the original data-sets. While a rigorous mathematical optimization for reducing the distances in distributions is not offered in this work, we opine that this prototype of an autonomous-agent generative complex social model is useful for studying and researching on pattern of life and anomaly detection where there is strict limitation or lack of sufficient data for using practical machine learning solutions in mission-critical applications.},
  archivePrefix = {arXiv},
  eprint = {2010.13026},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Sistrunk et al/Sistrunk et al_2020_On synthetic data generation for anomaly detection in complex social networks.pdf;/home/aleksandar/Zotero/storage/4B8IHPF5/2010.html},
  journal = {arXiv:2010.13026 [cs]},
  keywords = {Computer Science - Computers and Society,Computer Science - Social and Information Networks,G.3.3,G.3.4},
  primaryClass = {cs}
}

@article{sitEventHistoryAnalysis,
  title = {Event History Analysis of Dynamic Networks},
  author = {Sit, T. and Ying, Z. and Yu, Y.},
  doi = {10.1093/biomet/asaa045},
  abstract = {Summary.  Statistical analysis on networks has received growing attention due to demand from various emerging applications. In dynamic networks, one of the key},
  file = {/home/aleksandar/Zotero/storage/WWSUBM58/5910796.html},
  journal = {Biometrika},
  language = {en}
}

@article{skitkaAttitudeMoralizationProbably2018,
  title = {Attitude {{Moralization}}: {{Probably Not Intuitive}} or {{Rooted}} in {{Perceptions}} of {{Harm}}},
  shorttitle = {Attitude {{Moralization}}},
  author = {Skitka, Linda J. and Wisneski, Daniel C. and Brandt, Mark J.},
  year = {2018},
  month = feb,
  volume = {27},
  pages = {9--13},
  issn = {0963-7214},
  doi = {10.1177/0963721417727861},
  abstract = {People vary in the extent to which they imbue attitudes with moral conviction, and this variation is consequential. Yet we know relatively little about what makes people's feelings about a given attitude object transform from a relatively nonmoral preference to a moral conviction. In this article, we review evidence from two experiments and a field study that sheds some light on the processes that lead to attitude moralization. This research explored the roles of incidental and integral affect, cognitive factors such as recognition of harm, and whether attitude-moralization processes can occur outside conscious awareness or require some level of conscious deliberation. The findings present some challenges to contemporary theories that emphasize the roles of intuition and harm and indicate that more research designed to better understand moralization processes is needed.},
  journal = {Current Directions in Psychological Science},
  language = {en},
  number = {1}
}

@article{skoricGranovetterTheoryStrength2011,
  title = {Granovetter's Theory of Strength of Weak Ties},
  author = {Skoric, Marko and Tomasevic, Aleksandar},
  year = {2011},
  volume = {45},
  pages = {323--336},
  issn = {0085-6320},
  doi = {10.5937/socpreg1103323S},
  copyright = {All rights reserved},
  journal = {Socioloski pregled},
  language = {en},
  number = {3}
}

@article{sleepPerilsPartialingRedux2017,
  title = {Perils of Partialing Redux: {{The}} Case of the {{Dark Triad}}.},
  shorttitle = {Perils of Partialing Redux},
  author = {Sleep, Chelsea E. and Lynam, Donald R. and Hyatt, Courtland S. and Miller, Joshua D.},
  year = {2017},
  month = oct,
  volume = {126},
  pages = {939--950},
  issn = {1939-1846, 0021-843X},
  doi = {10.1037/abn0000278},
  journal = {Journal of Abnormal Psychology},
  language = {en},
  number = {7}
}

@article{slothuusHowPoliticalParties,
  title = {How {{Political Parties Shape Public Opinion}} in the {{Real World}}},
  author = {Slothuus, Rune and Bisgaard, Martin},
  volume = {n/a},
  issn = {1540-5907},
  doi = {10.1111/ajps.12550},
  abstract = {How powerful are political parties in shaping citizens' opinions? Despite long-standing interest in the flow of influence between partisan elites and citizens, few studies to date examine how citizens react when their party changes its position on a major issue in the real world. We present a rare quasi-experimental panel study of how citizens responded when their political party suddenly reversed its position on two major and salient welfare issues in Denmark. With a five-wave panel survey collected just around these two events, we show that citizens' policy opinions changed immediately and substantially when their party switched its policy position\textemdash even when the new position went against citizens' previously held views. These findings advance the current, largely experimental literature on partisan elite influence.},
  annotation = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/ajps.12550},
  copyright = {\textcopyright 2020, Midwest Political Science Association},
  file = {/home/aleksandar/Zotero/storage/FJY77NGC/ajps.html},
  journal = {American Journal of Political Science},
  language = {en},
  number = {n/a}
}

@techreport{smaldinoHowTranslateVerbal2020,
  title = {How to Translate a Verbal Theory into a Formal Model},
  author = {Smaldino, Paul},
  year = {2020},
  month = may,
  institution = {{MetaArXiv}},
  doi = {10.31222/osf.io/n7qsh},
  abstract = {Turning verbal theories into formal models is an essential business of a mature science. Here I elaborate on taxonomies of models, provide ten lessons for translating a verbal 3 theory into a formal model, and discuss the specific challenges involved in collaborations between modelers and non-modelers. It's a start.},
  file = {/home/aleksandar/Documents/Zotero/Smaldino/Smaldino_2020_How to translate a verbal theory into a formal model.pdf},
  language = {en},
  type = {Preprint}
}

@article{smithAcquiescentResponseBias2004,
  title = {Acquiescent {{Response Bias}} as an {{Aspect}} of {{Cultural Communication Style}}},
  author = {Smith, Peter B.},
  year = {2004},
  month = jan,
  volume = {35},
  pages = {50--61},
  issn = {0022-0221, 1552-5422},
  doi = {10.1177/0022022103260380},
  journal = {Journal of Cross-Cultural Psychology},
  language = {en},
  number = {1}
}

@article{smithElectionBoycottsRegime2009,
  title = {Election {{Boycotts}} and {{Regime Survival}}},
  author = {Smith, Ian Oliver},
  year = {2009},
  pages = {46},
  abstract = {Election boycotts are a common occurrence in unconsolidated democracies, particularly in the developing world, with prominent examples from recent years occurring in Venezuela, Zimbabwe, and Ethiopia. Despite the frequent occurrence of boycotts, there are few studies available in the scholarly literature concerning the effectiveness of electoral boycotts, particularly as a strategy of opposition parties seeking to bring about the end of electoral authoritarian governments. This paper is based in the democratization literature, with a particular focus on the behavior and vulnerabilities of hybrid or electoral authoritarian regimes. Using an original dataset with global coverage including hybrid regimes from 1981 to 2006, this paper uses event-history analysis to determine the efficacy of boycotts in national elections among other risk factors thought to undermine electoral authoritarian regimes as well as the possibilities for subsequent democratization occurring following both contested and boycotted electoral processes.},
  file = {/home/aleksandar/Dropbox/Zotero/Smith/Smith_2009_Election Boycotts and Regime Survival.pdf},
  language = {en}
}

@article{smithGuideChoosingCommunity2020,
  title = {A {{Guide}} for {{Choosing Community Detection Algorithms}} in {{Social Network Studies}}: {{The Question Alignment Approach}}},
  shorttitle = {A {{Guide}} for {{Choosing Community Detection Algorithms}} in {{Social Network Studies}}},
  author = {Smith, Natalie R. and Zivich, Paul N. and Frerichs, Leah M. and Moody, James and Aiello, Allison E.},
  year = {2020},
  month = oct,
  volume = {59},
  pages = {597--605},
  issn = {0749-3797},
  doi = {10.1016/j.amepre.2020.04.015},
  abstract = {Introduction Community detection, the process of identifying subgroups of highly connected individuals within a network, is an aspect of social network analysis that is relevant but potentially underutilized in prevention research. Guidance on using community detection methods stresses aligning methods with specific research questions but lacks clear operationalization. The Question Alignment approach was developed to help address this gap and promote the high-quality use of community detection methods. Methods A total of 6 community detection methods are discussed: Walktrap, Edge-Betweenness, Infomap, Louvain, Label Propagation, and Spinglass. The Question Alignment approach is described and demonstrated using real-world data collected in 2013. This hypothetical case study was conducted in 2019 and focused on targeting a hand hygiene intervention to high-risk communities to prevent influenza transmission. Results Community detection using the Walktrap method best fit the hypothetical case study. The communities derived using the Walktrap method were quite different from communities derived through the other 5 methods in both the number of communities and individuals within communities. There was evidence to support that the Question Alignment approach can help researchers produce more useful community detection results. Compared to other methods of selecting high-risk groups, the Walktrap produced the most communities that met the hypothetical intervention requirements. Conclusions As prevention research incorporating social networks increases, researchers can use the Question Alignment approach to produce more theoretically meaningful results and potentially more useful results for practice. Future research should focus on assessing whether the Question Alignment approach translates into improved intervention results.},
  file = {/home/aleksandar/Zotero/storage/M2AP93CM/S0749379720302166.html},
  journal = {American Journal of Preventive Medicine},
  language = {en},
  number = {4}
}

@article{smithMetalSubstitutionsIncarbonic1975,
  title = {Metal Substitutions Incarbonic Anhydrase: A Halide Ion Probe Study},
  shorttitle = {Metal Substitutions Incarbonic Anhydrase},
  author = {Smith, R. J. and Bryant, R. G.},
  year = {1975},
  month = oct,
  volume = {66},
  pages = {1281--1286},
  issn = {0006-291X},
  journal = {Biochemical and Biophysical Research Communications},
  language = {eng},
  number = {4},
  pmid = {3}
}

@article{smithSurfacedepthTheoryEmergence,
  title = {A Surface-Depth Theory of the Emergence of Complex Networks.},
  author = {Smith, Keith M.},
  doi = {arXiv:1902.00336v1},
  abstract = {{$<$}p{$>$}The broadly general characteristics of complex networks found across disciplines-- such as high clustering coefficients and heavy-tailed degree distributions-- has long invited the question of whether there are general generating mechanisms behind them. Here, we propose a theory of such mechanisms and undertake several experiments which validate it. This theory proposes that there are two key principles at work in the emergence of a network, constituting a `surface' factor and a `depth' factor making up the existence probability of network edges. The surface factor describes nodes as having tendencies for attachment which follow a log-normal distribution. The deep factor suggests that beneath these potentials for attachment there exist any number of important latent variables describing the nodes. These variables are formulated as a high-dimensional manifold and the `distances' between pairs of nodes on this manifold constitute a similarity weighting informing on the probability that any two nodes are connected. Using standard network measures, the topology of 110 networks across a variety of disciplines shows agreement with a simple two-parameter model based on this theory. Importantly, we show that the log-normal surface factor can explain the power-law-like degree distributions of sparse networks and, more strongly, the variety of degree distributions found across networks of different densities. We also demonstrate how inverting an estimated surface factor of a complete weighted world city network provides more plausible clusters of nodes than the original network based on geometric and cultural considerations. This theory proposes a new fundamental formulation of complex networks with wide reaching consequences throughout the multidisciplinary domain of complex systems. {$<$}/p{$>$}},
  journal = {arXiv Physics and Society}
}

@article{smithSurfacedepthTheoryEmergencea,
  title = {A Surface-Depth Theory of the Emergence of Complex Networks.},
  author = {Smith, Keith M.},
  doi = {arXiv:1902.00336v1},
  abstract = {{$<$}p{$>$}The broadly general characteristics of complex networks found across disciplines-- such as high clustering coefficients and heavy-tailed degree distributions-- has long invited the question of whether there are general generating mechanisms behind them. Here, we propose a theory of such mechanisms and undertake several experiments which validate it. This theory proposes that there are two key principles at work in the emergence of a network, constituting a `surface' factor and a `depth' factor making up the existence probability of network edges. The surface factor describes nodes as having tendencies for attachment which follow a log-normal distribution. The deep factor suggests that beneath these potentials for attachment there exist any number of important latent variables describing the nodes. These variables are formulated as a high-dimensional manifold and the `distances' between pairs of nodes on this manifold constitute a similarity weighting informing on the probability that any two nodes are connected. Using standard network measures, the topology of 110 networks across a variety of disciplines shows agreement with a simple two-parameter model based on this theory. Importantly, we show that the log-normal surface factor can explain the power-law-like degree distributions of sparse networks and, more strongly, the variety of degree distributions found across networks of different densities. We also demonstrate how inverting an estimated surface factor of a complete weighted world city network provides more plausible clusters of nodes than the original network based on geometric and cultural considerations. This theory proposes a new fundamental formulation of complex networks with wide reaching consequences throughout the multidisciplinary domain of complex systems. {$<$}/p{$>$}},
  journal = {arXiv Physics and Society}
}

@article{snijdersIntroductionStochasticActorbased2010,
  title = {Introduction to Stochastic Actor-Based Models for Network Dynamics},
  author = {Snijders, Tom A.B. and {van de Bunt}, Gerhard G. and Steglich, Christian E.G.},
  year = {2010},
  month = jan,
  volume = {32},
  pages = {44--60},
  issn = {03788733},
  doi = {10.1016/j.socnet.2009.02.004},
  journal = {Social Networks},
  language = {en},
  number = {1}
}

@article{sobkowiczHighEntropyAlloys2020,
  title = {High {{Entropy Alloys}} as a New Metaphor in Sociophysics},
  author = {Sobkowicz, Pawel},
  year = {2020},
  month = jul,
  abstract = {Most of the opinion dynamics models in sociophysics have their historic origin in studies two-dimensional magnetization phenomena. This metaphor has proven quite useful, as it allowed to use well known techniques, relating individual behaviours of single spins and their interactions, to large scale properties, such as magnetization or magnetic domains creation. These physical properties were then ``mapped'' to social concepts: spin orientation to a person's views on a specific issue, magnetization to global opinion on the issue, etc. During the past 20 years, the models were significantly expanded, using more complex individual agent characteristics and even more complex types of the interactions, but the power of the metaphor remained unchanged. In the current paper we propose to use a new physical system as the basis for new ideas in sociophysics. We shall argue that the concepts and tools devoted to studies of High Entropy Alloys (HEAs) could significantly broaden the range of social concepts ``addressable'' by sociophysics, by focusing on a wider range of global phenomena, arising from atomic properties, interactions and arrangements. We illustrate the new idea by calculating a few characteristics of a simple HEA system and their possible ``mapping'' into social concepts.},
  archivePrefix = {arXiv},
  eprint = {2007.02154},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Dropbox/Zotero/Sobkowicz/Sobkowicz_2020_High Entropy Alloys as a new metaphor in sociophysics.pdf;/home/aleksandar/Zotero/storage/IMBUIJ9T/2007.html},
  journal = {arXiv:2007.02154 [cond-mat, physics:physics]},
  primaryClass = {cond-mat, physics:physics}
}

@inproceedings{SocialSearchExplicit2009,
  title = {Towards {{Social Search}}- {{From Explicit}} to {{Implicit Collaboration}} to {{Predict Users}}' {{Interests}}:},
  shorttitle = {{{TOWARD SOCIAL SEARCH}} - {{From Explicit}} to {{Implicit Collaboration}} to {{Predict Users}}' {{Interests}}},
  booktitle = {Proceedings of the {{Fifth International Conference}} on {{Web Information Systems}} and {{Technologies}}},
  year = {2009},
  pages = {684--687},
  publisher = {{SciTePress - Science and and Technology Publications}},
  address = {{Lisbon, Portugal}},
  doi = {10.5220/0001841406840687},
  abstract = {Inspection methods and cheap self-reporting procedures have been significantly employed in the field of HumanComputer Interaction for assessing the usability of interfaces, systems and technologies. However, there is a tendency to overlook aspects related to the context and features of the users during the usability assessment process. This research introduces the concept of mental workload as an aid to enhance usability measurement. A user-study has been designed and executed in the context of human-web interaction. The aim was to investigate the relationship between the perception of usability of three popular web-sites, and the mental workload imposed by a set of typical tasks executed over them. Scores obtained with the System Usability Scale were compared to the mental workload scores obtained from the NASA Task Load Index and the Workload Profile assessment procedures. Findings suggest that perception of usability and mental workload are likely to be two nonoverlapping constructs, and there is no clear evidence of their interaction. They measure two different aspects of human-system interaction and therefore they could be jointly employed to better describe user experience.},
  file = {/home/aleksandar/Zotero/storage/A3SJDP8Z/2009 - TOWARD SOCIAL SEARCH - From Explicit to Implicit C.pdf},
  isbn = {978-989-8111-81-4},
  language = {en}
}

@article{sohnRobustComplexNetwork,
  title = {A Robust Complex Network Generation Method Based on Neural Networks},
  author = {Sohn, Insoo},
  doi = {10.1016/j.physa.2019.02.046},
  abstract = {{$<$}div{$><$}h2{$>$}Abstract{$<$}/h2{$><$}p{$>$}To enhance the network tolerance against numerous network attack strategies, various techniques to optimize conventional complex networks, such as scale-free networks, have been proposed. In this paper, we propose a new optimization technique based on artificial neural networks that is trained on scale-free network topologies as input data and hill climbing network topologies as output data. The goal of our method is to provide similar network robustness as the hill climbing network with much reduced complexity. Based on the experimental results, we demonstrate that the proposed network can provide strong robustness against both random and targeted attack, while significantly reduce optimization complexity.{$<$}/p{$><$}/div{$>$}},
  journal = {Physica A: Statistical Mechanics and its Applications}
}

@article{sohnRobustComplexNetworka,
  title = {A Robust Complex Network Generation Method Based on Neural Networks},
  author = {Sohn, Insoo},
  doi = {10.1016/j.physa.2019.02.046},
  abstract = {{$<$}div{$><$}h2{$>$}Abstract{$<$}/h2{$><$}p{$>$}To enhance the network tolerance against numerous network attack strategies, various techniques to optimize conventional complex networks, such as scale-free networks, have been proposed. In this paper, we propose a new optimization technique based on artificial neural networks that is trained on scale-free network topologies as input data and hill climbing network topologies as output data. The goal of our method is to provide similar network robustness as the hill climbing network with much reduced complexity. Based on the experimental results, we demonstrate that the proposed network can provide strong robustness against both random and targeted attack, while significantly reduce optimization complexity.{$<$}/p{$><$}/div{$>$}},
  journal = {Physica A: Statistical Mechanics and its Applications}
}

@article{sokolovskaAggressivenessHEXACOPersonality2018,
  title = {Aggressiveness in the {{HEXACO}} Personality Model},
  author = {Sokolovska, Valentina and Dini{\'c}, Bojana and Toma{\v s}evi{\'c}, Aleksandar},
  year = {2018},
  volume = {51},
  pages = {449--468},
  issn = {0048-5705, 1451-9283},
  doi = {10.2298/PSI170705022S},
  journal = {Psihologija},
  language = {en},
  number = {4}
}

@article{sokolovskaAggressivenessHEXACOPersonality2018a,
  title = {Aggressiveness in the {{HEXACO}} Personality Model},
  author = {Sokolovska, Valentina and Dini{\'c}, Bojana and Toma{\v s}evi{\'c}, Aleksandar},
  year = {2018},
  volume = {51},
  pages = {449--468},
  issn = {0048-5705, 1451-9283},
  doi = {10.2298/PSI170705022S},
  copyright = {All rights reserved},
  journal = {Psihologija},
  language = {en},
  number = {4}
}

@article{sokolovskaEvolutionStudentsFriendship2017,
  ids = {sokolovskaEvolutionStudentsFriendship2016},
  title = {Evolution of Students' Friendship Networks: {{Examining}} the Influence of Group Size},
  shorttitle = {Evolution of Students' Friendship Networks},
  author = {Sokolovska, Valentina and Toma{\v s}evi{\'c}, Aleksandar and Dini{\'c}, Bojana and Jari{\'c}, Isidora},
  year = {2017},
  month = jan,
  volume = {11},
  pages = {1135},
  issn = {2334-8801, 0353-1589},
  doi = {10.21301/eap.v11i4.10},
  copyright = {All rights reserved},
  journal = {Issues in Ethnology and Anthropology},
  number = {4}
}

@inproceedings{sokolovskaMeasuringSocialInequality2015,
  title = {Measuring Social Inequality: {{Comparasion}} of {{Gini}} Coefficient and {{Theil}} Index},
  booktitle = {All {{In One Conferences}} 2015},
  author = {Sokolovska, Valentina and Lazar, {\v Z}olt and Toma{\v s}evi{\'c}, Aleksandar},
  year = {2015},
  pages = {96--99},
  address = {{Dubai}},
  copyright = {All rights reserved}
}

@article{sokolovskaPozicijaUlogaAnalizi2012,
  title = {Pozicija i Uloga u Analizi Socijalnih Mre\v{z}a},
  author = {Sokolovska, Valentina and Toma{\v s}evi{\'c}, Aleksandar},
  year = {2012},
  volume = {37},
  pages = {153--165},
  journal = {Godi\v{s}njak Filozofskog fakulteta u Novom Sadu},
  number = {1}
}

@article{songLabelPropagationAlgorithm2019,
  title = {Label Propagation Algorithm Based on Node Similarity Driven by Local Information},
  author = {Song, Chen and Huang, Guoyan and Yin, Bo and Zhang, Bing and Liu, Xinqian},
  year = {2019},
  month = dec,
  volume = {33},
  pages = {1950363},
  issn = {0217-9792},
  doi = {10.1142/S0217979219503636},
  abstract = {Label propagation algorithm (LPA) attracts wide attention in community detection field for its near linear time complexity in large scale network. However, the algorithm adopts a random selection scheme in label updating strategy, which results in unstable division and poor accuracy. In this paper, five different indicators of node similarity are introduced based on network local information to distinguish nodes and a new label updating method is proposed. When there are multiple maximum neighbor labels in the propagation process, the maximum label corresponding to the most similar node is selected for updating instead of a random one. Five different forms of improved LPA are proposed which are named as SAL-LPA, SOR-LPA, JAC-LPA, SOR-LPA, HDI-LPA and HPI-LPA. The experiment results on real-world and artificial benchmark networks show that the improved LPA greatly improves the performance of the original algorithm, among which HPI-LPA is the best.},
  file = {/home/aleksandar/Dropbox/Zotero/Song et al/Song et al_2019_Label propagation algorithm based on node similarity driven by local information.pdf},
  journal = {International Journal of Modern Physics B},
  number = {30}
}

@article{spaltiEffectsDecisionTime2019,
  title = {The {{Effects}} of {{Decision Time}} on {{Perceptions}} of {{Decisions}} and {{Decision Makers}} in ({{Moral}}) {{Trade}}-{{Off Scenarios}}},
  author = {Sp{\"a}lti, Anna Katharina and Brandt, Mark and Zeelenberg, Marcel},
  year = {2019},
  month = jul,
  doi = {10.31234/osf.io/37t2r},
  abstract = {People often have to make trade-offs. We study three types of trade-offs: 1) "secular trade-offs" where no moral or sacred values are at stake, 2) "taboo trade-offs" where sacred values are pitted against financial gain, and 3) "tragic trade-offs" where sacred values are pitted against other sacred values. Previous research (Critcher et al., 2011; Tetlock et al., 2000) demonstrated that tragic and taboo trade-offs are not only evaluated by their outcomes, but are also evaluated based on the time it took to make the choice. We investigate two outstanding questions: 1) whether the effect of decision time differs for evaluations of decisions compared to decision makers and 2) whether moral contexts are unique in their ability to influence character evaluations through decision process information. In two experiments (total N = 1434) we find that decision time affects character evaluations, but not evaluations of the decision itself. There were no significant differences between tragic trade-offs and secular trade-offs, suggesting that the decisions structure may be more important in evaluations than moral context. Additionally, the magnitude of the effect of decision time shows us that decision time, may be of less practical use than expected. We thus urge, to take a closer examination of the processes underlying decision time and its perception.},
  file = {/home/aleksandar/Zotero/storage/NT39RQUB/37t2r.html}
}

@incollection{spampinatoImmunologicalAlgorithmGraph2020,
  title = {An {{Immunological Algorithm}} for {{Graph Modularity Optimization}}},
  booktitle = {Advances in {{Computational Intelligence Systems}}},
  author = {Spampinato, A. G. and Scollo, R. A. and Cavallaro, S. and Pavone, M. and Cutello, V.},
  editor = {Ju, Zhaojie and Yang, Longzhi and Yang, Chenguang and Gegov, Alexander and Zhou, Dalin},
  year = {2020},
  volume = {1043},
  pages = {235--247},
  publisher = {{Springer International Publishing}},
  address = {{Cham}},
  doi = {10.1007/978-3-030-29933-0_20},
  abstract = {Complex networks constitute the backbone of complex systems. They represent a powerful interpretation tool for describing and analyzing many different kinds of systems from biology, economics, engineering and social networks. Uncovering the community structure exhibited by real networks is a crucial step towards a better understanding of complex systems, revealing the internal organization of nodes. However, existing algorithms in the literature up-to-date present several crucial issues, and the question of how good an algorithm is, with respect to others, is still open. Recently, Newman [14] suggested modularity as a natural measure of the goodness of network community decompositions. Here we propose an implementation of an Immunological Algorithm, a population based computational systems inspired by the immune system and its features, to perform community detection on the methods of modularity maximization. The reliability and efficiency of the proposed algorithm has been validate by comparing it with Louvain algorithm one of the fastest and the popular algorithm based on a multiscale modularity optimization scheme.},
  file = {/home/aleksandar/Zotero/storage/5MQDHWGV/Spampinato et al. - 2020 - An Immunological Algorithm for Graph Modularity Op.pdf},
  isbn = {978-3-030-29932-3 978-3-030-29933-0},
  language = {en}
}

@article{spillerValidityCentralityHypothesis2020,
  title = {On the Validity of the Centrality Hypothesis in Cross-Sectional between-Subject Networks of Psychopathology},
  author = {Spiller, Tobias R. and Levi, Ofir and Neria, Yuval and {Suarez-Jimenez}, Benjamin and {Bar-Haim}, Yair and Lazarov, Amit},
  year = {2020},
  month = oct,
  volume = {18},
  pages = {297},
  issn = {1741-7015},
  doi = {10.1186/s12916-020-01740-5},
  abstract = {In the network approach to psychopathology, psychiatric disorders are considered networks of causally active symptoms (nodes), with node centrality hypothesized to reflect symptoms' causal influence within a network. Accordingly, centrality measures have been used in numerous network-based cross-sectional studies to identify specific treatment targets, based on the assumption that deactivating highly central nodes would proliferate to other nodes in the network, thereby collapsing the network structure and alleviating the overall psychopathology (i.e., the centrality hypothesis).},
  file = {/home/aleksandar/Documents/Zotero/Spiller et al/Spiller et al_2020_On the validity of the centrality hypothesis in cross-sectional between-subject.pdf;/home/aleksandar/Zotero/storage/XZCXCKCI/s12916-020-01740-5.html},
  journal = {BMC Medicine},
  number = {1}
}

@article{spirova2009ParliamentaryElections2010,
  title = {The 2009 Parliamentary Elections in {{Bulgaria}}},
  author = {Spirova, Maria},
  year = {2010},
  pages = {3},
  doi = {10.1016/j.electstud.2009.12.002},
  journal = {Electoral Studies},
  language = {en}
}

@article{spirovaParliamentaryElectionsBulgaria2006,
  title = {The Parliamentary Elections in {{Bulgaria}}, {{June}} 2005},
  author = {Spirova, Maria},
  year = {2006},
  month = sep,
  volume = {25},
  pages = {616--621},
  issn = {02613794},
  doi = {10.1016/j.electstud.2005.12.003},
  journal = {Electoral Studies},
  language = {en},
  number = {3}
}

@phdthesis{splinterElectoralCompetitionNew2020,
  title = {Electoral {{Competition}}: {{New Measures}} and {{Applications}}},
  shorttitle = {Electoral {{Competition}}},
  author = {Splinter, James},
  year = {2020},
  address = {{Ottawa, Ontario}},
  doi = {10.22215/etd/2020-13889},
  file = {/home/aleksandar/Dropbox/Zotero/Splinter/Splinter_2020_Electoral Competition.pdf},
  language = {en},
  school = {Carleton University},
  type = {Doctor of {{Philosophy}}}
}

@inproceedings{stankovicLowDimensionalityMethodDataDriven2020,
  title = {A {{Low}}-{{Dimensionality Method}} for {{Data}}-{{Driven Graph Learning}}},
  booktitle = {{{ICASSP}} 2020 - 2020 {{IEEE International Conference}} on {{Acoustics}}, {{Speech}} and {{Signal Processing}} ({{ICASSP}})},
  author = {Stankovi{\'c}, L. and Dakovi{\'c}, M. and Mandic, D. and Brajovi{\'c}, M. and {Scalzo-Dees}, B. and Constantinides, A. G.},
  year = {2020},
  month = may,
  pages = {5340--5344},
  issn = {2379-190X},
  doi = {10.1109/ICASSP40776.2020.9053641},
  abstract = {In many graph signal processing applications, finding the topology of a graph is part of the overall data processing problem rather than a priori knowledge. Most of the approaches to graph topology learning are based on the assumption of graph Laplacian sparsity, with various additional constraints, followed by variations of the edge weights in the graph domain or the eigenvalues in the graph spectral domain. These domains are high-dimensional, since their dimension is at least equal to the order of the number of vertices. In this paper, we propose a numerically efficient method for estimating of the normalized Laplacian through its eigenvalues estimation and by promoting its sparsity. The minimization problem is solved in quite a low-dimensional space, related to the polynomial order of the underlying system on a graph corresponding to the the observed data. The accuracy of the results is tested on numerical example.}
}

@article{stanleyEffectivenessIncentivesCompletion2020,
  title = {The {{Effectiveness}} of {{Incentives}} on {{Completion Rates}}, {{Data Quality}}, and {{Nonresponse Bias}} in a {{Probability}}-Based {{Internet Panel Survey}}},
  author = {Stanley, Marshica and Roycroft, Jessica and Amaya, Ashley and Dever, Jill A. and Srivastav, Anup},
  year = {2020},
  month = feb,
  pages = {1525822X20901802},
  publisher = {{SAGE Publications Inc}},
  issn = {1525-822X},
  doi = {10.1177/1525822X20901802},
  abstract = {Previous research has shown that increasing the size of incentives can increase response rates for probability-based, cross-sectional surveys. However, the effects of incentives on web panels have not been extensively studied. We sought to answer the question: What is the effect of larger, postpaid incentives on (1) response, (2) data quality, and (3) nonresponse bias for individuals in a web panel? We analyzed data from the 2015 and 2016 National Internet Flu Survey, a survey that uses the GfK KnowledgePanel\textregistered{} as its sampling frame. We compare panel members who received a postpaid, standard 1,000-point (the equivalent of US\$1) incentive in 2015 to panelists who received a larger, 5,000-point (the equivalent of US\$5) incentive in 2016. We found that larger incentives were associated with increased interview completion rates with minimal impact on data quality or bias.},
  journal = {Field Methods},
  language = {en}
}

@techreport{stanleyExposureOpposingReasons2020,
  title = {Exposure to {{Opposing Reasons Reduces Negative Impressions}} of {{Ideological Opponents}}},
  author = {Stanley, Matthew},
  year = {2020},
  month = jul,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/w9tp2},
  abstract = {Americans have become increasingly likely to dislike, distrust, and derogate their ideological opponents on contemporary social and political issues. We hypothesized that a lack of exposure to compelling reasons, arguments, and evidence from ideological opponents might at least partly explain negative views of those opponents. Consistent with this hypothesis, we found that participants assume their ideological opponents, in comparison to their ideological allies, are less likely to have good reasons for their positions. Moreover, we found that the more strongly participants believe their opponents lack good reasons for their positions, the more likely they are to report that those opponents lack both intellectual capabilities and moral character. Critically, exposure to arguments favoring their opponents' position produced more favorable impressions of those opponents. We discuss possible implications of these results for the role of reasons and reasoning in political discourse, and for productive disagreement in a functioning democracy.},
  file = {/home/aleksandar/Zotero/storage/I9J8KC93/Stanley - 2020 - Exposure to Opposing Reasons Reduces Negative Impr.pdf},
  language = {en},
  type = {Preprint}
}

@article{stanOppositionTakesCharge2005,
  title = {The Opposition Takes Charge : {{The Romanian}} General Elections of 2004},
  author = {Stan, Lavinia},
  year = {2005},
  volume = {52},
  pages = {3--15},
  doi = {10.1080/10758216.2005.11052203},
  journal = {Problems of Post-Communism},
  number = {3}
}

@article{starkRelativeDeprivationCause,
  ids = {starkRelativeDeprivationCausea},
  title = {Relative Deprivation as a Cause of Risky Behaviors},
  author = {Stark, Oded},
  doi = {10.1080/0022250X.2019.1664508},
  abstract = {Combining a standard measure of concern about low relative wealth and a standard measure of relative risk aversion leads to a novel explanation of variation in risk-taking behavior identified and documented by social psychologists and economists. We obtain two results: (1) Holding individual \emph{i}'s wealth and his rank in the wealth distribution constant, the individual's relative risk aversion decreases when he becomes more relatively deprived as a result of an increase in the average wealth of the individuals who are wealthier than he is. (2) If relative deprivation enters the individual's utility function approximately linearly then, holding constant individual \emph{i}'s wealth and the average wealth of the individuals who are wealthier than he is, the individual's relative risk aversion decreases when he becomes more relatively deprived as a result of a decline in his rank. Our findings provide a theoretical support for evidence about the propensity of relatively deprived individuals to gamble and resort to other risky behaviors.},
  journal = {Journal of Mathematical Sociology}
}

@misc{StatisticalRethinkingBayesian,
  title = {Statistical {{Rethinking}}: {{A Bayesian Course}} with {{Examples}} in {{R}} and {{STAN}}},
  shorttitle = {Statistical {{Rethinking}}},
  abstract = {Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated. This},
  file = {/home/aleksandar/Zotero/storage/Q8X4DKY5/9780367139919.html},
  howpublished = {https://www.crcpress.com/Statistical-Rethinking-A-Bayesian-Course-with-Examples-in-R-and-STAN/McElreath/p/book/9780367139919},
  journal = {CRC Press},
  language = {en}
}

@techreport{stefaniAreDimensionsGender2020,
  title = {Are {{Dimensions}} of {{Gender Inequality Uniformly Associated}} with {{Human Values}}?},
  author = {Stefani, Serena and Prati, Gabriele},
  year = {2020},
  month = apr,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/jacuw},
  abstract = {Gender inequality is an articulate and multidimensional phenomenon that affects various domains of existence. We started from this multidimensionality to understand how the relative importance of human values varies through the different dimensions of gender inequality - namely work, money, knowledge, time, power, and health. Through a crosssectional design that includes 18 European countries and the use of Bayesian correlational analysis, the findings seemed to outline three groups of values: (1) universalism, benevolence and self-direction are strongly and positively correlated to gender equality; (2) security, power and achievement are strongly and negatively correlated to equality while (3) conformity, tradition, stimulation, and hedonism have weak/non-significant correlation coefficients with gender equality. Relevance to cultural values and ideologies that support social equality are discussed. Furthermore, the specificities of some values find an explanation in relation to certain dimensions. The widening of the perspective on gender equality rectifies the previous findings by outlining a more complex scenario than the one previously identified, where the results included only two groups of values.},
  language = {en},
  type = {Preprint}
}

@article{steinleyMethodMakingInferences2017a,
  ids = {steinleyMethodMakingInferences2017},
  title = {A Method for Making Inferences in Network Analysis: {{Comment}} on {{Forbes}}, {{Wright}}, {{Markon}}, and {{Krueger}} (2017).},
  shorttitle = {A Method for Making Inferences in Network Analysis},
  author = {Steinley, Douglas and Hoffman, Michaela and Brusco, Michael J. and Sher, Kenneth J.},
  year = {2017},
  month = oct,
  volume = {126},
  pages = {1000--1010},
  issn = {1939-1846, 0021-843X},
  doi = {10.1037/abn0000308},
  journal = {Journal of Abnormal Psychology},
  language = {en},
  number = {7}
}

@article{steinmetzAnalyzingObservedComposite2013,
  title = {Analyzing {{Observed Composite Differences}} across {{Groups}}},
  author = {Steinmetz, Holger},
  year = {2013},
  pages = {41},
  abstract = {Although the use of structural equation modeling has increased during the last decades, the typical procedure to investigate mean differences across groups is still to create an observed composite score from several indicators and to compare the composite's mean across the groups. Whereas the structural equation modeling literature has emphasized that a comparison of latent means presupposes equal factor loadings and indicator intercepts for most of the indicators (i.e., partial invariance), it is still unknown if partial invariance is sufficient when relying on observed composites. This Monte Carlo study investigated whether one or two unequal factor loadings and indicator intercepts in a composite can lead to wrong conclusions regarding latent mean differences. Results show that unequal indicator intercepts substantially affect the composite mean difference and the probability of a significant composite difference. In contrast, unequal factor loadings demonstrate only small effects. It is concluded that analyses of composite differences are only warranted in conditions of full measurement invariance, and the author recommends the analyses of latent mean differences with structural equation modeling instead.},
  file = {/home/aleksandar/Dropbox/Zotero/Steinmetz/Steinmetz_2013_Analyzing Observed Composite Differences across Groups.pdf},
  language = {en}
}

@inproceedings{stephanyaCodingTogetherCodingAlone2019,
  title = {Coding {{Together}}-{{Coding Alone}}: {{The Role}} of {{Trust}} in {{Collaborative Programming}}},
  shorttitle = {Coding {{Together}}-{{Coding Alone}}},
  author = {Stephanya, Fabian and Braesemannb, Fabian and Grahamb, Mark},
  year = {2019},
  doi = {10.31235/osf.io/8rf2h},
  abstract = {In the digital economy, innovation processes increasingly rely on highly specialised know-how and open-source software shared on digital platforms on collaborative programming. The information that feeds into the content on these platforms is provided voluntarily by a vast crowd of knowledgeable users from all over the world. In contributing to the platforms, users invest their time and share knowledge with strangers to add to the rising body of digital knowledge. This requires an open mindset and trust. In this study, we argue that such a mindset is not just an individual asset, but determined by the local communities the users are embedded in. We, therefore, hypothesise that places with higher levels of trust should contribute more to Stack Overflow, the world's largest question-and-answer platform for programming questions. In relating the city-level contributions of 266 OECD metropolitan areas to infrastructure, economic, and trust measures, we find this hypothesis confirmed. In contrast, click rates to the platform are solely driven by infrastructure and economic variables, but not by trust. These findings highlight the importance of societal values in the 21st century knowledge economy: if policymakers want to develop a lively local digital economy, it is not enough to provide fast Internet access and business opportunities. Instead, it is equally important to establish a trust-building environment that fosters sharing of innovative ideas, collaborations, and knowledge spillovers. JEL CLASSIFICATION J 24, L 17, O 35, R 12},
  file = {/home/aleksandar/Zotero/storage/Y5XYEUW5/Stephanya et al_2019_Coding Together-Coding Alone2.pdf},
  keywords = {DONE}
}

@article{stewartCollectiveDecisionsNetworks2020,
  title = {Collective {{Decisions}} on {{Networks}}: Information Biased and Voting Coordination},
  author = {Stewart, Alexander J and Mosleh, Mohsen and Diakonova, Marina and Arechar, Antonio and Rand, David G and Plotkin, Joshua B},
  year = {2020},
  pages = {4},
  file = {/home/aleksandar/Zotero/storage/WSDABEIS/Stewart et al. - 2020 - Collective Decisions on Networks information bias.pdf},
  language = {en}
}

@book{stimsonTidesConsentHow2004,
  title = {Tides of Consent: How Public Opinion Shapes {{American}} Politics},
  shorttitle = {Tides of Consent},
  author = {Stimson, James A.},
  year = {2004},
  publisher = {{Cambridge University Press}},
  address = {{New York}},
  isbn = {978-0-521-84134-4 978-0-521-60117-7},
  keywords = {Politics and government Public opinion,Public opinion,United States},
  lccn = {HN90.P8 S845 2004}
}

@article{stivalaUsingSampledNetwork2020,
  title = {Using {{Sampled Network Data With The Autologistic Actor Attribute Model}}},
  author = {Stivala, Alex D. and Gallagher, H. Colin and Rolls, David A. and Wang, Peng and Robins, Garry L.},
  year = {2020},
  month = feb,
  abstract = {Social science research increasingly benefits from statistical methods for understanding the structured nature of social life, including for social network data. However, the application of statistical network models within large-scale community research is hindered by too little understanding of the validity of their inferences under realistic data collection conditions, including sampled or missing network data. The autologistic actor attribute model (ALAAM) is a statistical model based on the well-established exponential random graph model (ERGM) for social networks. ALAAMs can be regarded as a social influence model, predicting an individual-level outcome based on the actor's network ties, concurrent outcomes of his/her network partners, and attributes of the actor and his/her network partners. In particular, an ALAAM can be used to measure contagion effects, that is, the propensity of two actors connected by a social network tie to both have the same value of an attribute. We investigate the effect of using simple random samples and snowball samples of network data on ALAAM parameter inference, and find that parameter inference can still work well even with a nontrivial fraction of missing nodes. However it is safer to take a snowball sample of the network and estimate conditional on the snowball sampling structure.},
  archivePrefix = {arXiv},
  eprint = {2002.00849},
  eprinttype = {arxiv},
  journal = {arXiv:2002.00849 [cs, stat]},
  language = {en},
  primaryClass = {cs, stat}
}

@article{stoetzerForecastingElectionsMultiparty2019,
  ids = {stoetzerForecastingElectionsMultiparty,stoetzerForecastingElectionsMultiparty2019a,stoetzerForecastingElectionsMultipartya,stoetzerForecastingElectionsMultipartyb,stoetzerForecastingElectionsMultipartyc,stoetzerForecastingElectionsMultipartyd},
  title = {Forecasting {{Elections}} in {{Multiparty Systems}}: {{A Bayesian Approach Combining Polls}} and {{Fundamentals}}},
  shorttitle = {Forecasting {{Elections}} in {{Multiparty Systems}}},
  author = {Stoetzer, Lukas F. and Neunhoeffer, Marcel and Gschwend, Thomas and Munzert, Simon and Sternberg, Sebastian},
  year = {2019},
  month = apr,
  volume = {27},
  pages = {255--262},
  issn = {1047-1987, 1476-4989},
  doi = {10.1017/pan.2018.49},
  file = {/home/aleksandar/Zotero/storage/E9WB35T6/CA929544F672A09A0E34C5529EBFA482.html},
  journal = {Political Analysis},
  language = {en},
  number = {2}
}

@incollection{stojiljkovicPartijskiSistemSrbije2006,
  title = {Partijski Sistem {{Srbije}}},
  booktitle = {Partije i Izbori u {{Srbiji}} - 20 Godina},
  author = {Stojiljkovi{\'c}, Zoran},
  editor = {Orlovi{\'c}, Slavi{\v s}a},
  year = {2006},
  pages = {7--11},
  publisher = {{\v{C}igoja \v{s}tampa}},
  address = {{Beograd}},
  isbn = {978-86-7549-450-8}
}

@incollection{stojiljkovicSocijalniRascepiPolje2011,
  title = {Socijalni Rascepi i Polje Politike},
  booktitle = {Partije i Izbori u {{Srbiji}} - 20 Godina},
  author = {Stojiljkovi{\'c}, Zoran},
  editor = {Orlovi{\'c}, Slavi{\v s}a},
  year = {2011},
  pages = {71--104},
  publisher = {{\v{C}igoja \v{s}tampa}},
  address = {{Beograd}}
}

@article{stokesPartyLoyaltyLikelihood1962,
  title = {Party Loyalty and the Likelihood of Deviating Elections},
  author = {Stokes, Donald E.},
  year = {1962},
  month = nov,
  volume = {24},
  pages = {689--702},
  issn = {0022-3816, 1468-2508},
  doi = {10.1017/S0022381600016182},
  journal = {The Journal of Politics},
  language = {en},
  number = {4}
}

@article{stoltzCulturalCartographyWord2020,
  title = {Cultural {{Cartography}} with {{Word Embeddings}}},
  author = {Stoltz, Dustin S. and Taylor, Marshall A.},
  year = {2020},
  month = jul,
  abstract = {Using the presence or frequency of keywords is a classic approach in the formal analysis of text, but has the drawback of glossing over the relationality of word meanings. Word embedding models overcome this problem by constructing a standardized meaning space where words are assigned a location based on relations of similarity to, and difference from, other words based on how they are used in natural language samples. We show how word embeddings can be put to the task of interpretation via two kinds of navigation. First, one can hold terms constant and measure how the embedding space moves around them--much like astronomers measured the changing of celestial bodies with the seasons. Second, one can also hold the embedding space constant and see how documents or authors move relative to it--just as ships use the stars on a given night to determine their location. Using the empirical case of immigration discourse in the United States, we demonstrate the merits of these two broad strategies to advance formal approaches to cultural analysis.},
  archivePrefix = {arXiv},
  eprint = {2007.04508},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Dropbox/Zotero/Stoltz_Taylor/Stoltz_Taylor_2020_Cultural Cartography with Word Embeddings.pdf;/home/aleksandar/Zotero/storage/IRSY7KLU/2007.html},
  journal = {arXiv:2007.04508 [cs]},
  primaryClass = {cs}
}

@article{strachanBivariateModelDichotomous2020,
  title = {Bivariate {{Model}} for {{Dichotomous Responses}} and {{Latent Variables Jointly Assessing Attitude}} and {{Attitudinal Stability}}},
  author = {Strachan, Tyler and Ip, Edward H.},
  year = {2020},
  month = may,
  volume = {0},
  pages = {1--15},
  publisher = {{Routledge}},
  issn = {0027-3171},
  doi = {10.1080/00273171.2020.1762064},
  abstract = {In any given survey, individuals are likely to differ in attitudes toward the subject matter. They also may differ in terms of the duration and persistence of attitudes, with some persons' beliefs being much more stable than others. For the purpose of jointly assessing attitude and temporal attitudinal stability, we propose a latent bivariate item response model. Attitudinal stability is operationalized as a construct called response consistency, which is indicated by the concordance of observed responses between two-time points. A simulation experiment assesses the parameter recovery of the proposed model. A real data analysis example uses data collected from a study on folklore beliefs about diabetes (563 individuals from multiple rural communities in North Carolina). On two different occasions, the individuals in the sample completed a 31-item common-sense model of diabetes inventory, which measures the congruence of their beliefs with a biomedical model. Results from the simulation study showed that the model parameters and factor correlation in the latent bivariate IRT model overall recovered well. Results from the real data analysis demonstrated the saliency of the construct. A weak association between having beliefs congruent with the biomedical model and response consistency across the two administrations was found.},
  annotation = {\_eprint: https://doi.org/10.1080/00273171.2020.1762064},
  journal = {Multivariate Behavioral Research},
  number = {0},
  pmid = {32401552}
}

@book{strangLinearAlgebraIts,
  title = {Linear {{Algebra}} and {{Its Applications}}},
  author = {Strang, Gilbert},
  file = {/home/aleksandar/Dropbox/Zotero/Strang/Strang_Linear Algebra and Its Applications.pdf},
  language = {en}
}

@article{straulinoBidirectionalApproachComparing2020,
  title = {A Bi-Directional Approach to Comparing the Modular Structure of Networks},
  author = {Straulino, Daniel and Landman, Mattie and O'Clery, Neave},
  year = {2020},
  month = oct,
  abstract = {Here we propose a new method to compare the modular structure of a pair of node-aligned networks. The majority of current methods, such as normalized mutual information, compare two node partitions derived from a community detection algorithm yet ignore the respective underlying network topologies. Addressing this gap, our method deploys a community detection quality function to assess the fit of each node partition with respect to the other network's connectivity structure. Specifically, for two networks A and B, we project the node partition of B onto the connectivity structure of A. By evaluating the fit of B's partition relative to A's own partition on network A (using a standard quality function), we quantify how well network A describes the modular structure of B. Repeating this in the other direction, we obtain a two-dimensional distance measure, the bi-directional (BiDir) distance. The advantages of our methodology are three-fold. First, it is adaptable to a wide class of community detection algorithms that seek to optimize an objective function. Second, it takes into account the network structure, specifically the strength of the connections within and between communities, and can thus capture differences between networks with similar partitions but where one of them might have a more defined or robust community structure. Third, it can also identify cases in which dissimilar optimal partitions hide the fact that the underlying community structure of both networks is relatively similar. We illustrate our method for a variety of community detection algorithms, including multi-resolution approaches, and a range of both simulated and real world networks.},
  archivePrefix = {arXiv},
  eprint = {2010.06568},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Straulino et al/Straulino et al_2020_A bi-directional approach to comparing the modular structure of networks.pdf;/home/aleksandar/Zotero/storage/YMTHHGPZ/2010.html},
  journal = {arXiv:2010.06568 [physics, q-fin]},
  primaryClass = {physics, q-fin}
}

@article{stukalBotsAutocratsHow,
  title = {Bots for {{Autocrats}}: {{How Pro}}-{{Government Bots Fight Opposition}} in {{Russia}}},
  author = {Stukal, Denis and Sanovich, Sergey and Bonneau, Richard and Tucker, Joshua A},
  pages = {55},
  abstract = {Abundant anecdotal evidence suggests that non-democracies employ new digital technologies known as social media bots to facilitate policy goals both domestically and abroad. However, few previous attempts have been made to systematically analyze the strategies behind the political use of bots. This paper seeks to fill this gap by developing two alternative frameworks \textendash{} referred to as the offline demobilization and online agenda control frameworks \textendash{} for theorizing about the role of bots in non-democratic regimes. We test various empirical implications of these two frameworks using a large collection of Twitter data generated by Russian pro-government bots in response to offline and online Russian domestic opposition activities. We show that although predictions generated from both frameworks receive empirical support, there is more evidence consistent with the online agenda control framework. These results have implications for the theories of state propaganda and disinformation employed by modern non-democratic regimes.},
  file = {/home/aleksandar/Zotero/storage/WY858F3Y/Stukal et al. - Bots for Autocrats How Pro-Government Bots Fight .pdf},
  language = {en}
}

@article{stulhoferDeterminantsHomonegativityEurope2009,
  title = {Determinants of {{Homonegativity}} in {{Europe}}},
  author = {{\v S}tulhofer, Aleksander and Rimac, Ivan},
  year = {2009},
  month = feb,
  volume = {46},
  pages = {24--32},
  issn = {0022-4499},
  doi = {10.1080/00224490802398373},
  abstract = {This paper analyzes macro-determinants of negative attitudes toward homosexuality in 31 European societies. Using the European Values Study (EVS) 1999/2000, carried out on national probability samples, we tested the effects of tradition, modernization, and postmodernization on homonegativity. According to the findings, social distance toward homosexual persons was predicted by Eastern Orthodox religion, a greater degree of urbanization, economic development, and immigration. Disapproval of homosexuality was found associated with economic development and urbanization, while immigration had borderline significance. Although the analyses pointed to the central role of the modernization processes in increasing social acceptance of homosexuality in European societies, persisting religious traditions and more recent immigration waves limit the effect of modernization.},
  journal = {The Journal of Sex Research},
  number = {1},
  pmid = {19012059}
}

@article{sugishitaOpinionDynamicsTiedecay2020,
  title = {Opinion Dynamics in Tie-Decay Networks},
  author = {Sugishita, Kashin and Porter, Mason A. and {Beguerisse-D{\'i}az}, Mariano and Masuda, Naoki},
  year = {2020},
  month = sep,
  abstract = {In social networks, interaction patterns typically change over time. We study opinion dynamics in tie-decay networks, in which tie strength increases instantaneously when there is an interaction and decays exponentially between interactions. Specifically, we formulate continuous-time Laplacian dynamics and a discrete-time DeGroot model of opinion dynamics in tie-decay networks, and we carry out numerical computations for the continuous-time Laplacian dynamics. We examine the speed of convergence by studying the spectral gap of combinatorial Laplacian matrices of tie-decay networks. First, we examine the spectral gaps of tie-decay networks from empirical data. Intuitively, we expect small tie-decay rates to lead to fast convergence, because the influence of each interaction event between a pair of nodes lasts longer for smaller decay rates. Moreover, as time progresses and more interaction events occur, we expect to observe convergence to proceed. However, we demonstrate that the spectral gaps need not decrease monotonically with respect to the decay rate or increase monotonically with respect to time. Second, we compare the spectral gaps of these empirical networks with corresponding randomized and aggregate networks. We find that the spectral gaps of the empirical networks tend to be smaller than those of the randomized and aggregate networks. This highlights the importance of the interplay between the times that edges arise and decay in temporal networks.},
  archivePrefix = {arXiv},
  eprint = {2010.00143},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Sugishita et al/Sugishita et al_2020_Opinion dynamics in tie-decay networks.pdf;/home/aleksandar/Zotero/storage/W5FSU72I/2010.html},
  journal = {arXiv:2010.00143 [nlin, physics:physics]},
  primaryClass = {nlin, physics:physics}
}

@article{sunCommunityDetectionBased2020,
  title = {Community Detection Based on the {{Matthew}} Effect},
  author = {Sun, Zejun and Sun, Yanan and Chang, Xinfeng and Wang, Qiming and Yan, Xuetong and Pan, Zhongqiang and Li, Zong-ping},
  year = {2020},
  month = oct,
  volume = {205},
  pages = {106256},
  issn = {0950-7051},
  doi = {10.1016/j.knosys.2020.106256},
  abstract = {Community structure exists in most real-world networks, such as social networks, smart grids, and transportation networks. Established approaches for community detection usually depend on some user-defined criteria (e.g., minimum cut, normalized cut, modularity, etc.). These criteria-based methods usually involve some optimization procedures and need to specify some parameters, which are thus time consuming and sensitive to parameters. In this paper, inspired by the Mathew effect of human society, we view a network as a social system and design a new algorithm called CDME (community detection based on the Matthew effect). Relying on the new concept, CDME has many desirable properties. It allows uncovering high-quality communities driven by dynamic CDME is also parameter free. More importantly, since CDME works in a local way and only needs to calculate the attractiveness of neighboring nodes, which lend itself to handling large-scale networks. Experiments on both synthetic and real-world data sets have demonstrated that CDME has many benefits and outperforms many state-of-the-art algorithms.},
  file = {/home/aleksandar/Zotero/storage/J25DURNV/S0950705120304500.html},
  journal = {Knowledge-Based Systems},
  language = {en}
}

@inproceedings{sunGameTheoreticalApproach2019,
  title = {Game {{Theoretical Approach}} for {{Non}}-{{Overlapping Community Detection}}},
  booktitle = {2019 5th {{International Conference}} on {{Big Data Computing}} and {{Communications}} ({{BIGCOM}})},
  author = {Sun, Baohua and {Al-Bayaty}, Richard and Huang, Qiuyuan and Wu, Dapeng},
  year = {2019},
  month = aug,
  pages = {222--230},
  issn = {null},
  doi = {10.1109/BIGCOM.2019.00038},
  abstract = {Graph clustering, i.e., partitioning nodes or data points into non-overlapping clusters, can be beneficial in a large varieties of computer vision and machine learning applications. However, main graph clustering schemes such as spectral clustering cannot be applied to a large network due to prohibitive computational complexity required. While there exist methods applicable to large networks, these methods do not offer convincing comparisons against known ground truth. For the first time, this work conducts clustering algorithm performance evaluations on large networks (consisting of one million nodes) with ground truth information. Ideas and concepts from game theory are applied towards graph clustering to formulate a new proposed algorithm, Game Theoretical Approach for Clustering (GTAC). This theoretical framework is shown to be a generalization of both the Label Propagation and Louvain methods, offering an additional means of derivation and analysis. GTAC introduces a tuning parameter which allows variable algorithm performance in accordance with application needs. Experimentation shows that these GTAC algorithms offer scalability and tunability towards big data applications.}
}

@article{sutcliffeComputationalModellingTrust2010,
  title = {Computational {{Modelling}} of {{Trust}} and {{Social Relationships}}},
  author = {Sutcliffe, Alistair and Wang, Di},
  year = {2010},
  volume = {15},
  pages = {3},
  issn = {1460-7425},
  file = {/home/aleksandar/Dropbox/Zotero/Sutcliffe_Wang/Sutcliffe_Wang_2010_Computational Modelling of Trust and Social Relationships.pdf;/home/aleksandar/Zotero/storage/WAW4UVRS/3.html},
  journal = {Journal of Artificial Societies and Social Simulation},
  number = {1}
}

@article{suzukiEfficientCommunicationIndexicality,
  title = {Efficient Communication and Indexicality},
  author = {Suzuki, Toru},
  doi = {10.1016/j.mathsocsci.2020.06.002},
  abstract = {{$<$}p{$>$}Since sending explicit messages can be costly, people often utilize "what is not said," i.e., informative silence, to economize communication. This paper studies the efficient communication rule, which is fully informative while minimizing the use of explicit messages, in cooperative environments. It is shown that when the notion of context is defined as the finest mutually self-evident event that contains the current state, the efficient use of informative silence exhibits the defining property of indexicals in natural languages. While the efficient use of silence could be complex, it is also found that the efficient use of silence can be as "simple" as the use of indexicals in natural languages if and only if the information structure satisfies some centrality and dominance properties.{$<$}/p{$>$}},
  journal = {Mathematical Social Science}
}

@article{swidlerCommentStephenVaisey2008,
  title = {Comment on {{Stephen Vaisey}}'s ``{{Socrates}}, {{Skinner}}, and {{Aristotle}}: {{Three Ways}} of {{Thinking About Culture}} in {{Action}}''},
  shorttitle = {Comment on {{Stephen Vaisey}}'s ``{{Socrates}}, {{Skinner}}, and {{Aristotle}}},
  author = {Swidler, Ann},
  year = {2008},
  month = sep,
  volume = {23},
  pages = {614--618},
  issn = {08848971, 15737861},
  doi = {10.1111/j.1573-7861.2008.00080.x},
  journal = {Sociological Forum},
  language = {en},
  number = {3}
}

@article{swidlerCultureActionSymbols1986,
  title = {Culture in {{Action}}: {{Symbols}} and {{Strategies}}},
  author = {Swidler, Ann},
  year = {1986},
  volume = {51},
  pages = {273--286},
  issn = {00031224},
  doi = {10.2307/2095521},
  abstract = {[Culture influences action not by providing the ultimate values toward which action is oriented, but by shaping a repertoire or "tool kit" of habits, skills, and styles from which people construct "strategies of action." Two models of cultural influence are developed, for settled and unsettled cultural periods. In settled periods, culture independently influences action, but only by providing resources from which people can construct diverse lines of action. In unsettled cultural periods, explicit ideologies directly govern action, but structural opportunities for action determine which among competing ideologies survive in the long run. This alternative view of culture offers new opportunities for systematic, differentiated arguments about culture's causal role in shaping action.]},
  journal = {American Sociological Review},
  number = {2}
}

@article{swimelarNationalismEuropeanizationLGBT2019,
  title = {Nationalism and {{Europeanization}} in {{LGBT Rights}} and {{Politics}}: {{A Comparative Study}} of {{Croatia}} and {{Serbia}}},
  shorttitle = {Nationalism and {{Europeanization}} in {{LGBT Rights}} and {{Politics}}},
  author = {Swimelar, Safia},
  year = {2019},
  month = aug,
  volume = {33},
  pages = {603--630},
  issn = {0888-3254, 1533-8371},
  doi = {10.1177/0888325418805135},
  abstract = {LGBT rights have come to be seen as allied with the idea of ``Europe'' and a European identity, particularly in the process of European Union enlargement to the East. Scholars have examined the ways in which external norms interact with more local, often ``traditional'' norms and identities. In this process, nationalism and conceptions of national identity and gender/sexuality norms can be seen as important factors that influence the domestic adoption of LGBT rights, particularly in the post-war Balkans. Croatia and Serbia (from approximately 2000 to 2014) present two interesting and different cases to analyze how discourses and dynamics of national and state identity construction, nationalism, and LGBT rights relate to discourses of ``Europeanness'' and European identity and how these affect the political dynamics of LGBT rights. This article finds that in Croatia, national identity was constructed in terms of convergence with European norms and identity, homonationalism was used to distinguish themselves from a ``Balkan'' identity, and there was a lower threat perception of the LGBT community framed primarily as a ``threat to the family.'' In Serbia, state and national identity was constructed in opposition to Europe and homosexuality had stronger threat perception, framed primarily as ``threat to the nation.'' In short, nationalism and national identity were less disadvantageous as a domestic constraint to LGBT rights in Croatia than in Serbia. The dynamics between nationalism and LGBT rights played out, for example, in the politics of the marriage referendum, Pride Parades, and public discourse more generally. This research contributes to the scholarship on LGBT rights and nationalism by empirically analyzing the different ways that nationalism, gender/sexuality, and European identity interrelate and influence LGBT rights change in a changing post-war identity landscape and how domestic constraints affect human rights norm diffusion.},
  journal = {East European Politics and Societies: and Cultures},
  language = {en},
  number = {3}
}

@article{sznajd-weronOpinionEvolutionClosed2000a,
  title = {Opinion Evolution in Closed Community},
  author = {{Sznajd-Weron}, Katarzyna and Sznajd, J.},
  year = {2000},
  abstract = {A simple Ising spin model which can describe a mechanism of making a decision in a closed community is proposed. It is shown via standard Monte Carlo simulations that very simple rules lead to rather complicated dynamics and to a power law in the decision time distribution. It is found that a closed community has to evolve either to a dictatorship or a stalemate state (inability to take any common decision). A common decision can be taken in a "democratic way" only by an open community.}
}

@article{taberyNetworkStructureTrust2020,
  title = {The Network Structure of Trust in the {{COVID}}-19 Pandemic},
  author = {Tabery, Paulina and Pilnacek, Matous},
  year = {2020},
  month = oct,
  volume = {0},
  pages = {1--15},
  publisher = {{Routledge}},
  issn = {1461-6696},
  doi = {10.1080/14616696.2020.1834597},
  abstract = {Trust is considered an essential part of a democratic society. That is why it is important to pay attention to it, especially in times of crisis. Some recent studies have introduced a new approach to examining attitudes and trust using social network analysis. The aim of our study is to examine the relationship between different types of trust during the coronavirus crisis. Using a sample representative of the adult population of the Czech Republic, our text discusses what the network structure of trust is like during the current pandemic. We find that the institutions or actors that are directly involved in resolving the crisis, such as security institutions and medical professionals, are located at the centre of the network of trust.},
  annotation = {\_eprint: https://doi.org/10.1080/14616696.2020.1834597},
  file = {/home/aleksandar/Documents/Zotero/Tabery_Pilnacek/Tabery_Pilnacek_2020_The network structure of trust in the COVID-19 pandemic.pdf;/home/aleksandar/Zotero/storage/XNZUFACJ/14616696.2020.html},
  journal = {European Societies},
  keywords = {coronavirus crisis,network analysis,Trust},
  number = {0}
}

@inproceedings{takanoEmergenceTradeoffLaw2020,
  title = {Emergence of the Tradeoff Law of Social Relationships in Artificial Societies Driven by Dual Memory Mechanisms},
  booktitle = {The 2020 {{Conference}} on {{Artificial Life}}},
  author = {Takano, Masanori and Nakazato, Kenichi},
  year = {2020},
  pages = {87--94},
  publisher = {{MIT Press}},
  address = {{Online}},
  doi = {10.1162/isal_a_00255},
  abstract = {The trade-off between the number and closeness of friendships is one of the important features of communication systems. This distinguishes asynchronous text communication through the Internet (lightweight communications) from the face-to-face communication and social grooming of primates (elaborate communications). In this study, we modeled communication as messaging flows driven by edge and node memory mechanisms in order to investigate micro-mechanisms that realize the trade-off law and the differences between the two types of communications. Five patterns of social structures including the trade-off law emerged depending on the strengths of the memory mechanisms. This suggests how communication systems construct different social structures. These results provide insight into the design of online social networks.},
  file = {/home/aleksandar/Zotero/storage/ARJX7R2M/Takano and Nakazato - 2020 - Emergence of the tradeoff law of social relationsh.pdf},
  language = {en}
}

@article{talagaHomophilyProcessGenerating2019,
  title = {Homophily as a Process Generating Social Networks: Insights from {{Social Distance Attachment}} Model},
  shorttitle = {Homophily as a Process Generating Social Networks},
  author = {Talaga, Szymon and Nowak, Andrzej},
  year = {2019},
  month = jul,
  abstract = {Real-world social networks often exhibit high levels of clustering, positive degree assortativity, short average path lengths (small-world property) and bounded, which means not scale-free, degree distributions. At the same time homophily, defined as the propensity of similar agents to connect to each other, is one of the characteristic features observed in many human and animal societies. In this paper we examine an extent to which homophily may be considered an important force generating and structuring social networks. To do so, we conduct a simulation study based on Social Distance Attachment (SDA) network model in which nodes are embedded in an abstract social space and connection probabilities depend functionally on distances between nodes. We find that homophily, especially when combined with random edge rewiring, can reproduce many of the characteristic features of social networks. Additionally, we devise a hybrid model combining SDA with configuration model that allows to generate homophily-driven networks with arbitrary degree distributions and we use it to study interactions of homophily with processes imposing constraints on degree distributions. We show that the effects of homophily on clustering are robust with respect to distribution constraints, while degree assortativity can be highly dependent on the particular kind of enforced degree sequence. Numerical results are additionally enriched with a review of theoretical and/or analytical arguments supporting both the general geometric approach to social networks and homophily as well as the particular functional form of the connection probability function assumed in SDA model.},
  archivePrefix = {arXiv},
  eprint = {1907.07055},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Zotero/storage/52KLURSG/1907.html},
  journal = {arXiv:1907.07055 [physics]},
  primaryClass = {physics}
}

@article{tandonCommunityDetectionNetworks2020,
  title = {Community Detection in Networks Using Graph Embeddings},
  author = {Tandon, Aditya and Albeshri, Aiiad and Thayananthan, Vijey and Alhalabi, Wadee and Radicchi, Filippo and Fortunato, Santo},
  year = {2020},
  month = sep,
  abstract = {Graph embedding methods are becoming increasingly popular in the machine learning community, where they are widely used for tasks such as node classification and link prediction. Embedding graphs in geometric spaces should aid the identification of network communities as well, because nodes in the same community should be projected close to each other in the geometric space, where they can be detected via standard data clustering algorithms. In this paper, we test the ability of several graph embedding techniques to detect communities on benchmark graphs. We compare their performance against that of traditional community detection algorithms. We find that the performance is comparable, if the parameters of the embedding techniques are suitably chosen. However, the optimal parameter set varies with the specific features of the benchmark graphs, like their size, whereas popular community detection algorithms do not require any parameter. So it is not possible to indicate beforehand good parameter sets for the analysis of real networks. This finding, along with the high computational cost of embedding a network and grouping the points, suggests that, for community detection, current embedding techniques do not represent an improvement over network clustering algorithms.},
  archivePrefix = {arXiv},
  eprint = {2009.05265},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Tandon et al/Tandon et al_2020_Community detection in networks using graph embeddings.pdf;/home/aleksandar/Zotero/storage/WTMETJ2V/2009.html},
  journal = {arXiv:2009.05265 [physics]},
  primaryClass = {physics}
}

@article{tangSemidefiniteProgrammingBased2020,
  title = {Semidefinite Programming Based Community Detection for Node-Attributed Networks and Multiplex Networks},
  author = {Tang, Fengqin and Wang, Chunning and Su, Jinxia and Wang, Yuanyuan},
  year = {2020},
  month = nov,
  volume = {0},
  pages = {1--17},
  publisher = {{Taylor \& Francis}},
  issn = {0361-0918},
  doi = {10.1080/03610918.2020.1847291},
  abstract = {Community detection is an effective exploration technique for analyzing networks. Most of the network data not only describes the connections of network nodes but also describes the properties of the nodes. In this paper, we propose a community detection method collects relevant evidences from the information of node attributes and the information of network structure to assist the community detection task on node-attributed networks. We find communities in the framework of the semidefinite programming (SDP) method. In practical applications, the distribution of some node attributes may be uncorrelated with the network structure or the network itself may contain no communities as in a random graph. A sparse attribute self-adjustment mechanism is introduced to determine the relative importance of each source of information. As a by product, our method is also effective for community detection of multilayer networks that allow for multiple kinds of relations over the same set of nodes. Experimental results demonstrate the effectiveness of the proposed method.},
  annotation = {\_eprint: https://doi.org/10.1080/03610918.2020.1847291},
  file = {/home/aleksandar/Zotero/storage/NUBN5LH4/03610918.2020.html},
  journal = {Communications in Statistics - Simulation and Computation},
  keywords = {62-07,62P25,Community detection,Multiplex networks,Semidefinite programming,Stochastic block model},
  number = {0}
}

@book{tannebergPoliticsRepressionAuthoritarian2020,
  title = {The {{Politics}} of {{Repression Under Authoritarian Rule}}: {{How Steadfast}} Is the {{Iron Throne}}?},
  shorttitle = {The {{Politics}} of {{Repression Under Authoritarian Rule}}},
  author = {Tanneberg, Dag},
  year = {2020},
  publisher = {{Springer International Publishing}},
  address = {{Cham}},
  doi = {10.1007/978-3-030-35477-0},
  isbn = {978-3-030-35476-3 978-3-030-35477-0},
  language = {en},
  series = {Contributions to {{Political Science}}}
}

@article{tarentinoComparisonSubstrateSpecificities1975,
  title = {A Comparison of the Substrate Specificities of Endo-Beta-{{N}}-Acetylglucosaminidases from {{Streptomyces}} Griseus and {{Diplococcus Pneumoniae}}},
  author = {Tarentino, A. L. and Maley, F.},
  year = {1975},
  month = nov,
  volume = {67},
  pages = {455--462},
  issn = {0006-291X},
  journal = {Biochemical and Biophysical Research Communications},
  language = {eng},
  number = {1},
  pmid = {1016}
}

@article{taspinarBayesianInferenceSpatial,
  title = {Bayesian {{Inference}} for {{Spatial Stochastic Volatility Models}}},
  author = {Tasp{\i}nar, Suleyman and Do, Osman and Bera, Anil K},
  pages = {32},
  abstract = {In this study, we propose a spatial stochastic volatility model in which the latent log-volatility terms follow a spatial autoregressive process. Though there is no spatial correlation in the outcome equation (the mean equation), the spatial autoregressive process defined for the logvolatility terms introduces spatial dependence in the outcome equation. To introduce a Bayesian Markov chain Monte Carlo (MCMC) estimation algorithm, we transform the model so that the outcome equation takes the form of log-squared terms. We approximate the distribution of the log-squared error terms in the outcome equation with a finite mixture of normal distributions so that the transformed model turns into a linear Gaussian state-space model. Our simulation results indicate that the Bayesian estimator has satisfactory finite sample properties. We investigate the practical usefulness of our proposed model and estimation method by using the price returns of residential properties in the broader Chicago Metropolitan area.},
  language = {en}
}

@article{tavitsDevelopmentStableParty2005,
  title = {The Development of Stable Party Support: {{Electoral}} Dynamics in Post-Communist {{Europe}}},
  shorttitle = {The {{Development}} of {{Stable Party Support}}},
  author = {Tavits, Margit},
  year = {2005},
  month = apr,
  volume = {49},
  pages = {283--298},
  issn = {0092-5853, 1540-5907},
  doi = {10.1111/j.0092-5853.2005.00123.x},
  journal = {American Journal of Political Science},
  language = {en},
  number = {2}
}

@article{tavitsLinkageElectoralVolatility2008,
  title = {On the Linkage between Electoral Volatility and Party System Instability in {{Central}} and {{Eastern Europe}}},
  author = {Tavits, Margit},
  year = {2008},
  month = aug,
  volume = {47},
  pages = {537--555},
  issn = {03044130, 14756765},
  doi = {10.1111/j.1475-6765.2008.00782.x},
  journal = {European Journal of Political Research},
  language = {en},
  number = {5}
}

@article{taylorConceptClassAnalysis2020,
  title = {Concept {{Class Analysis}}: {{A Method}} for {{Identifying Cultural Schemas}} in {{Texts}}},
  shorttitle = {Concept {{Class Analysis}}},
  author = {Taylor, Marshall and Stoltz, Dustin},
  year = {2020},
  volume = {7},
  pages = {544--569},
  issn = {23306696},
  doi = {10.15195/v7.a23},
  journal = {Sociological Science}
}

@article{taylorConceptClassAnalysis2020a,
  title = {Concept {{Class Analysis}}: {{A Method}} for {{Identifying Cultural Schemas}} in {{Texts}}},
  shorttitle = {Concept {{Class Analysis}}},
  author = {Taylor, Marshall and Stoltz, Dustin},
  year = {2020},
  volume = {7},
  pages = {544--569},
  issn = {23306696},
  doi = {10.15195/v7.a23},
  abstract = {Recent methodological work at the intersection of culture, cognition, and computational methods has drawn attention to how cultural schemas can be ``recovered'' from social survey data. Defining cultural schemas as slowly learned, implicit, and unevenly distributed relational memory structures, researchers show how schemas\textemdash or rather, the downstream consequences of people drawing upon them\textemdash can be operationalized and measured from domain-specific survey modules. Respondents can then be sorted into ``classes'' on the basis of the schema to which their survey response patterns best align. In this article, we extend this ``schematic class analysis'' method to text data. We introduce concept class analysis (CoCA): a hybrid model that combines word embeddings and correlational class analysis to group documents across a corpus by the similarity of schemas recovered from them. We introduce the CoCA model, illustrate its validity and utility using simulations, and conclude with considerations for future research and applications.},
  file = {/home/aleksandar/Zotero/storage/ZLW3DEUZ/Taylor and Stoltz - 2020 - Concept Class Analysis A Method for Identifying C.pdf},
  journal = {Sociological Science},
  language = {en}
}

@article{taylorExploringHowHomophily2018,
  title = {Exploring {{How Homophily}} and {{Accessibility Can Facilitate Polarization}} in {{Social Networks}}},
  author = {Taylor, Cameron E. and Mantzaris, Alexander V. and Garibay, Ivan},
  year = {2018},
  month = dec,
  volume = {9},
  pages = {325},
  publisher = {{Multidisciplinary Digital Publishing Institute}},
  doi = {10.3390/info9120325},
  abstract = {Polarization in online social networks has gathered a significant amount of attention in the research community and in the public sphere due to stark disagreements with millions of participants on topics surrounding politics, climate, the economy and other areas where an agreement is required. This work investigates into greater depth a type of model that can produce ideological segregation as a result of polarization depending on the strength of homophily and the ability of users to access similar minded individuals. Whether increased access can induce larger amounts of societal separation is important to investigate, and this work sheds further insight into the phenomenon. Center to the hypothesis of homophilic alignments in friendship generation is that of a discussion group or community. These are modeled and the investigation into their effect on the dynamics of polarization is presented. The social implications demonstrate that initial phases of an ideological exchange can result in increased polarization, although a consensus in the long run is expected and that the separation between groups is amplified when groups are constructed with ideological homophilic preferences.},
  copyright = {http://creativecommons.org/licenses/by/3.0/},
  file = {/home/aleksandar/Dropbox/Zotero/Taylor et al/Taylor et al_2018_Exploring How Homophily and Accessibility Can Facilitate Polarization in Social.pdf;/home/aleksandar/Zotero/storage/9EN4XYRB/325.html},
  journal = {Information},
  language = {en},
  number = {12}
}

@article{taylorHighDimensionalCovariance,
  title = {High-dimensional Covariance Estimation for {{Gaussian}} Directed Acyclic Graph Models with given Order},
  author = {Taylor, Jerome and Khare, Kshitij},
  doi = {10.1002/wics.1468},
  abstract = {{$<$}p{$>$}The covariance matrix is a fundamental quantity that helps us understand the nature of relationships among variables in a multivariate data set. Estimating the covariance matrix can be challenging in modern applications where the number of variables is often larger than the number of samples. In this paper, we review methods which tackle this challenge by inducing sparsity in the Cholesky parameter of the inverse covariance matrix.{$<$}/p{$>$} {$<$}p{$>$}This article is categorized under:          {$<$}/p{$><$}ul{$>$} {$<$}li{$>$}Algorithms and Computational Methods \> Numerical Methods{$<$}/li{$>$} {$<$}li{$>$}Statistical and Graphical Methods of Data Analysis \> Multivariate Analysis{$<$}/li{$>$} {$<$}li{$>$}Statistical and Graphical Methods of Data Analysis \> Analysis of High Dimensional Data{$<$}/li{$>$} {$<$}/ul{$>$}},
  journal = {Wiley Interdisciplinary Reviews: Computational Statistics}
}

@article{taylorHighDimensionalCovariancea,
  title = {High-dimensional Covariance Estimation for {{Gaussian}} Directed Acyclic Graph Models with given Order},
  author = {Taylor, Jerome and Khare, Kshitij},
  doi = {10.1002/wics.1468},
  abstract = {{$<$}p{$>$}The covariance matrix is a fundamental quantity that helps us understand the nature of relationships among variables in a multivariate data set. Estimating the covariance matrix can be challenging in modern applications where the number of variables is often larger than the number of samples. In this paper, we review methods which tackle this challenge by inducing sparsity in the Cholesky parameter of the inverse covariance matrix.{$<$}/p{$>$} {$<$}p{$>$}This article is categorized under:          {$<$}/p{$><$}ul{$>$} {$<$}li{$>$}Algorithms and Computational Methods \> Numerical Methods{$<$}/li{$>$} {$<$}li{$>$}Statistical and Graphical Methods of Data Analysis \> Multivariate Analysis{$<$}/li{$>$} {$<$}li{$>$}Statistical and Graphical Methods of Data Analysis \> Analysis of High Dimensional Data{$<$}/li{$>$} {$<$}/ul{$>$}},
  journal = {Wiley Interdisciplinary Reviews: Computational Statistics}
}

@article{tenenhausGeneAssociationNetworks2010,
  title = {Gene Association Networks from Microarray Data Using a Regularized Estimation of Partial Correlation Based on {{PLS}} Regression},
  author = {Tenenhaus, Arthur and Guillemot, Vincent and Gidrol, Xavier and Frouin, Vincent},
  year = {2010},
  month = apr,
  volume = {7},
  pages = {251--262},
  issn = {1545-5963},
  doi = {10.1109/TCBB.2008.87},
  journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
  number = {2}
}

@article{tennerAccumulationChartsInstantrunoff,
  title = {Accumulation Charts for Instant-Runoff Elections. ({{arXiv}}:1903.06095v1 [Physics.Soc-Ph])},
  author = {Tenner{$<$}/a{$>$}, {$<$}a href="http://arxiv.org/find/physics/1/au:+Tenner\_B/0/1/0/all/0/1"{$>$}Bridget Eileen and Warrington{$<$}/a{$>$}, {$<$}a href="http://arxiv.org/find/physics/1/au:+Warrington\_G/0/1/0/all/0/1"{$>$}Gregory S.},
  doi = {arXiv:1903.06095v1},
  abstract = {{$<$}p{$>$}We propose a new graphical format for instant-runoff voting election results. We call this proposal an "accumulation chart." This model, a modification of standard bar charts, is easy to understand, clearly indicates the winner, depicts the instant-runoff algorithm, and summarizes the votes cast. Moreover, it includes the pedigree of each accumulated vote and gives a clear depiction of candidates' coalitions. {$<$}/p{$>$}},
  journal = {arXiv Computer Science}
}

@article{tennerAccumulationChartsInstantrunoffa,
  title = {Accumulation Charts for Instant-Runoff Elections. ({{arXiv}}:1903.06095v1 [Physics.Soc-Ph])},
  author = {Tenner{$<$}/a{$>$}, {$<$}a href="http://arxiv.org/find/physics/1/au:+Tenner\_B/0/1/0/all/0/1"{$>$}Bridget Eileen and Warrington{$<$}/a{$>$}, {$<$}a href="http://arxiv.org/find/physics/1/au:+Warrington\_G/0/1/0/all/0/1"{$>$}Gregory S.},
  doi = {arXiv:1903.06095v1},
  abstract = {{$<$}p{$>$}We propose a new graphical format for instant-runoff voting election results. We call this proposal an "accumulation chart." This model, a modification of standard bar charts, is easy to understand, clearly indicates the winner, depicts the instant-runoff algorithm, and summarizes the votes cast. Moreover, it includes the pedigree of each accumulated vote and gives a clear depiction of candidates' coalitions. {$<$}/p{$>$}},
  journal = {arXiv Computer Science}
}

@article{theocharisComputationalSocialScience2020,
  title = {Computational {{Social Science}} and the {{Study}} of {{Political Communication}}},
  author = {Theocharis, Yannis and Jungherr, Andreas},
  year = {2020},
  month = oct,
  volume = {0},
  pages = {1--22},
  publisher = {{Routledge}},
  issn = {1058-4609},
  doi = {10.1080/10584609.2020.1833121},
  abstract = {The challenge of disentangling political communication processes and their effects has grown with the complexity of the new political information environment. But so have scientists' toolsets and capacities to better study and understand them. We map the challenges and opportunities of developing, synthesizing, and applying data collection and analysis techniques relying primarily on computational methods and tools to answer substantive theory-driven questions in the field of political communication. We foreground the theoretical, empirical, and institutional opportunities and challenges of Computational Communication Science (CCS) that are relevant to the political communication community. We also assess understandings of CCS and highlight challenges associated with data and resource requirements, as well as those connected with the theory and semantics of digital signals. With an eye to existing practices, we elaborate on the key role of infrastructures, academic institutions, ethics, and training in computational methods. Finally, we present the six full articles and two forum contributions of this special issue illustrating methodological innovation, as well as the theoretical, practical, and institutional relevance and challenges for realizing the potential of computational methods in political communication..},
  annotation = {\_eprint: https://doi.org/10.1080/10584609.2020.1833121},
  file = {/home/aleksandar/Documents/Zotero/Theocharis_Jungherr/Theocharis_Jungherr_2020_Computational Social Science and the Study of Political Communication.pdf;/home/aleksandar/Zotero/storage/AYIGMJQL/10584609.2020.html},
  journal = {Political Communication},
  number = {0}
}

@article{theodorCytotoxicInteractionGorgonian1975,
  title = {Cytotoxic Interaction between Gorgonian Explants: Mode of Action},
  shorttitle = {Cytotoxic Interaction between Gorgonian Explants},
  author = {Theodor, J. L. and Senelar, R.},
  year = {1975},
  month = oct,
  volume = {19},
  pages = {194--200},
  issn = {0008-8749},
  journal = {Cellular Immunology},
  language = {eng},
  number = {2},
  pmid = {147}
}

@article{thiessonARMATimeSeriesModeling2012,
  title = {{{ARMA Time}}-{{Series Modeling}} with {{Graphical Models}}},
  author = {Thiesson, Bo and Chickering, David Maxwell and Heckerman, David and Meek, Christopher},
  year = {2012},
  month = aug,
  abstract = {We express the classic ARMA time-series model as a directed graphical model. In doing so, we find that the deterministic relationships in the model make it effectively impossible to use the EM algorithm for learning model parameters. To remedy this problem, we replace the deterministic relationships with Gaussian distributions having a small variance, yielding the stochastic ARMA (ARMA) model. This modification allows us to use the EM algorithm to learn parmeters and to forecast,even in situations where some data is missing. This modification, in conjunction with the graphicalmodel approach, also allows us to include cross predictors in situations where there are multiple times series and/or additional nontemporal covariates. More surprising,experiments suggest that the move to stochastic ARMA yields improved accuracy through better smoothing. We demonstrate improvements afforded by cross prediction and better smoothing on real data.},
  archivePrefix = {arXiv},
  eprint = {1207.4162},
  eprinttype = {arxiv},
  journal = {arXiv:1207.4162 [cs, stat]},
  primaryClass = {cs, stat}
}

@article{thoemmesAssumptionsDirectionDependence,
  title = {The {{Assumptions}} of {{Direction Dependence Analysis}}},
  author = {Thoemmes, Felix},
  doi = {10.1080/00273171.2019.1608800},
  abstract = {Direction dependence analysis attempts to discern the direction of a causal effect, using statistical features of the data, such as skew and kurtosis of variables, and their residuals in regression models. Wiedermann and Sebastian discuss the use of this analysis in the context of mediation, and introduce methods to distinguish three different causal structures. In this commentary, I highlight some connections to literature in computer science, review the assumptions of the proposed analysis critically, and provide an example in which I argue that the analysis of Wiedermann and Sebastian can yield incorrect conclusions.},
  journal = {Multivariate Behavioral Research}
}

@article{thoemmesAssumptionsDirectionDependencea,
  title = {The {{Assumptions}} of {{Direction Dependence Analysis}}},
  author = {Thoemmes, Felix},
  doi = {10.1080/00273171.2019.1608800},
  abstract = {Direction dependence analysis attempts to discern the direction of a causal effect, using statistical features of the data, such as skew and kurtosis of variables, and their residuals in regression models. Wiedermann and Sebastian discuss the use of this analysis in the context of mediation, and introduce methods to distinguish three different causal structures. In this commentary, I highlight some connections to literature in computer science, review the assumptions of the proposed analysis critically, and provide an example in which I argue that the analysis of Wiedermann and Sebastian can yield incorrect conclusions.},
  journal = {Multivariate Behavioral Research}
}

@article{thorlaksonMultiLevelElectoralPolitics,
  title = {Multi-{{Level Electoral Politics}}: {{Beyond}} the {{Second}}-{{Order Election Model Sona N}}. {{Golder}}, {{Ignacio Lago}}, {{Andr\'e Blais}}, {{Elisabeth Gidengil}}, {{Thomas Gschwend}}, {{Oxford}}: {{Oxford University Press}}, 2018, Pp. 240.},
  author = {Thorlakson, Lori},
  doi = {10.1017/S0008423918000951},
  abstract = {//static.cambridge.org/content/id/urn\%3Acambridge.org\%3Aid\%3Aarticle\%3AS0008423918000951/resource/name/firstPage-S0008423918000951a.jpg},
  journal = {Canadian Journal of Political Science}
}

@article{thorlaksonMultiLevelElectoralPoliticsa,
  title = {Multi-{{Level Electoral Politics}}: {{Beyond}} the {{Second}}-{{Order Election Model Sona N}}. {{Golder}}, {{Ignacio Lago}}, {{Andr\'e Blais}}, {{Elisabeth Gidengil}}, {{Thomas Gschwend}}, {{Oxford}}: {{Oxford University Press}}, 2018, Pp. 240.},
  author = {Thorlakson, Lori},
  doi = {10.1017/S0008423918000951},
  abstract = {//static.cambridge.org/content/id/urn\%3Acambridge.org\%3Aid\%3Aarticle\%3AS0008423918000951/resource/name/firstPage-S0008423918000951a.jpg},
  journal = {Canadian Journal of Political Science}
}

@article{thorlaksonMultiLevelElectoralPoliticsb,
  title = {Multi-{{Level Electoral Politics}}: {{Beyond}} the {{Second}}-{{Order Election Model Sona N}}. {{Golder}}, {{Ignacio Lago}}, {{Andr\'e Blais}}, {{Elisabeth Gidengil}}, {{Thomas Gschwend}}, {{Oxford}}: {{Oxford University Press}}, 2018, Pp. 240.},
  author = {Thorlakson, Lori},
  doi = {10.1017/S0008423918000951},
  abstract = {//static.cambridge.org/content/id/urn\%3Acambridge.org\%3Aid\%3Aarticle\%3AS0008423918000951/resource/name/firstPage-S0008423918000951a.jpg},
  journal = {Canadian Journal of Political Science}
}

@misc{tianUnifiedBayesianModel2020,
  title = {A {{Unified Bayesian Model}} for {{Generalized Community Detection}} in {{Attribute Networks}}},
  author = {Tian, Qiang and Wang, Wenjun and Xie, Yingjie and Wu, Huaming and Jiao, Pengfei and Pan, Lin},
  year = {2020},
  month = aug,
  volume = {2020},
  pages = {e5712815},
  publisher = {{Hindawi}},
  issn = {1076-2787},
  doi = {10.1155/2020/5712815},
  abstract = {Identification of community structures and the underlying semantic characteristics of communities are essential tasks in complex network analysis. However, most methods proposed so far are typically only applicable to assortative community structures, that is, more links within communities and fewer links between different communities, which ignore the rich diversity of community regularities in real networks. In addition, the node attributes that provide rich semantics information of communities and networks can facilitate in-depth community detection of structural information. In this paper, we propose a novel unified Bayesian generative model to detect generalized communities and provide semantic descriptions simultaneously by combining network topology and node attributes. The proposed model is composed of two closely correlated parts by a transition matrix; we first apply the concept of a mixture model to describe network regularities and then adjust the classic Latent Dirichlet Allocation (LDA) topic model to identify community semantically. Thus, the model can detect broad types of network structure regularities, including assortative structures, disassortative structures, and mixture structures and provide multiple semantic descriptions for the communities. To optimize the objective function of the model, we use an effective Gibbs sampling algorithm. Experiments on a number of synthetic and real networks show that our model has superior performance compared with some baselines on community detection.},
  file = {/home/aleksandar/Zotero/storage/TRK9Q986/Tian et al_2020_A Unified Bayesian Model for Generalized Community Detection in Attribute.pdf;/home/aleksandar/Zotero/storage/ZW3PUVHZ/5712815.html},
  howpublished = {https://www.hindawi.com/journals/complexity/2020/5712815/},
  journal = {Complexity},
  language = {en},
  type = {Research {{Article}}}
}

@inproceedings{tianxingMultilevelIntelligentAssistant2020,
  title = {A {{Multilevel Intelligent Assistant}} for {{Multilevel Social Network Analysis}}},
  booktitle = {2020 {{IEEE}} 10th {{International Conference}} on {{Intelligent Systems}} ({{IS}})},
  author = {Tianxing, Man and Zhukova, Nataly and Tsochev, Georgi},
  year = {2020},
  month = aug,
  pages = {404--408},
  issn = {1541-1672},
  doi = {10.1109/IS48319.2020.9199840},
  abstract = {In recent years, social network data analysis is an emerging field. Multilevel social network analysis could help researchers to figure out comprehensive influencing factors. But the raw data extracted from life is complicated and inoperable so that the entire analysis process is multi-step and changeable. The characteristics of social network datasets at different levels are diverse. There is no general data analysis algorithms for each level. The characteristics of the data and the requirements of the task are important basis for choosing suitable analysis methods. But it is hard for non-expert researchers. This paper proposes an intelligent multilevel assistant for multilevel social network analysis and implements it based on ontology technology. Algorithms at different levels process the corresponding data forms until the appropriate output model is generated. The use of ontology technology makes this framework extensible and understandable. Such a framework is significant for non-experts.},
  file = {/home/aleksandar/Zotero/storage/MI8SPCY2/9199840.html}
}

@article{tibshiraniRegressionShrinkageSelection1996,
  title = {Regression {{Shrinkage}} and {{Selection Via}} the {{Lasso}}},
  author = {Tibshirani, Robert},
  year = {1996},
  month = jan,
  volume = {58},
  pages = {267--288},
  issn = {00359246},
  doi = {10.1111/j.2517-6161.1996.tb02080.x},
  journal = {Journal of the Royal Statistical Society: Series B (Methodological)},
  language = {en},
  number = {1}
}

@inproceedings{tioConstructingGraphicalModels2020,
  title = {Constructing {{Graphical Models}} for {{Multi}}-{{Source Data}}: {{Sparse Network}} and {{Component Analysis}}},
  shorttitle = {Constructing {{Graphical Models}} for {{Multi}}-{{Source Data}}},
  booktitle = {Advanced {{Studies}} in {{Classification}} and {{Data Science}}},
  author = {Tio, Pia and Waldorp, Lourens and VanDeun, Katrijn},
  editor = {Imaizumi, Tadashi and Okada, Akinori and Miyamoto, Sadaaki and Sakaori, Fumitake and Yamamoto, Yoshiro and Vichi, Maurizio},
  year = {2020},
  pages = {275--287},
  publisher = {{Springer}},
  address = {{Singapore}},
  doi = {10.1007/978-981-15-3311-2_22},
  abstract = {Gaussian graphical models (GGMs) are a popular method for analysing complex data by modelling the unique relationships between variables. Recently, a shift in interest has taken place from investigating relationships within a (sub)discipline (e.g. genetics) to estimating relationships between variables from various subdisciplines (e.g. how gene expression relates to cognitive performance). It is thus not surprising that there is an increasing need for analysing large, so-called multi-source datasets, each containing detailed information from many data sources on the same individuals. GGMs are a straightforward statistical candidate for estimating unique cross-source relationships from such network-oriented data. However, the multi-source nature of the data poses two challenges: First, different sources may inherently differ from one another, biasing the estimation of the relations. Second, GGMs are not cut out for separating cross-source relationships from all other, source-specific relationships. In this paper we propose the addition of a simultaneous-component-model pre-processing step to the Gaussian graphical model, the combination of which is suitable for estimating cross-source relationships from multi-source data.