text:
'dominant' to begin with. Further, if the rebel is dominant, then, with the rebel becoming stronger (i.e., becoming more cost efficient), the level of rebel activity increases, while the security-based measures by the government is lowered. Thus, our analysis suggests that the trade-off is a nuanced one. This framework also allows us to reconcile some divergent results in the empirical literature regarding the effect of development works on conflict.},
  journal = {International Game Theory Review}
}

@article{martinImpactNetworkSize2020,
  title = {On the Impact of Network Size and Average Degree on the Robustness of Centrality Measures},
  author = {Martin, Christoph and Niemeyer, Peter},
  year = {2020},
  month = oct,
  pages = {1--22},
  issn = {2050-1242, 2050-1250},
  doi = {10.1017/nws.2020.37},
  abstract = {Measurement errors are omnipresent in network data. Most studies observe an erroneous network instead of the desired error-free network. It is well known that such errors can have a severe impact on network metrics, especially on centrality measures: a central node in the observed network might be less central in the underlying, error-free network. The robustness is a common concept to measure these effects. Studies have shown that the robustness primarily depends on the centrality measure, the type of error (e.g., missing edges or missing nodes), and the network topology (e.g., tree-like, core-periphery). Previous findings regarding the influence of network size on the robustness are, however, inconclusive. We present empirical evidence and analytical arguments indicating that there exist arbitrary large robust and non-robust networks and that the average degree is well suited to explain the robustness. We demonstrate that networks with a higher average degree are often more robust. For the degree centrality and Erdo\H{}s\textendash R\'enyi (ER) graphs, we present explicit formulas for the computation of the robustness, mainly based on the joint distribution of node degrees and degree changes which allow us to analyze the robustness for ER graphs with a constant average degree or increasing average degree.},
  file = {/home/aleksandar/Zotero/storage/94FUQTMA/Martin and Niemeyer - 2020 - On the impact of network size and average degree o.pdf},
  journal = {Network Science},
  language = {en}
}

@article{martiniPoliticalSupportWinners2019,
  title = {Political Support among Winners and Losers: {{Within}}- and between-Country Effects of Structure, Process and Performance in {{Europe}}: {{POLITICAL SUPPORT AMONG WINNERS AND LOSERS}}},
  shorttitle = {Political Support among Winners and Losers},
  author = {Martini, Sergio and Quaranta, Mario},
  year = {2019},
  month = feb,
  volume = {58},
  pages = {341--361},
  issn = {03044130},
  doi = {10.1111/1475-6765.12284},
  journal = {European Journal of Political Research},
  language = {en},
  number = {1}
}

@techreport{martinReliabilityFactorModeling2020,
  title = {The {{Reliability Factor}}: {{Modeling}} Individual Reliability with Multiple Items from a Single Assessment},
  shorttitle = {The {{Reliability Factor}}},
  author = {Martin, Stephen Ross and Rast, Philippe},
  year = {2020},
  month = jan,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/kr4xq},
  abstract = {Reliability is a crucial concept in psychometrics. Although it is typically estimated as a single fixed quantity, previous work suggests that reliability can vary across persons, groups, and covariates. We propose a novel method for estimating and modeling case-specific reliability without repeated measurements or parallel tests. The proposed method employs a ``Reliability Factor'' that models the error variance of each case across multiple indicators, thereby producing case-specific reliability estimates. Additionally, we use Gaussian process modeling to a estimate non-linear, non-monotonic function between the latent factor itself and the reliability of the measure, providing an analogue to test information functions in item response theory. The reliability factor model is a new tool for examining latent regions with poor conditional reliability, and correlates thereof, in a classical test theory framework.},
  language = {en},
  type = {Preprint}
}

@article{marwanHowAvoidPotential2011,
  title = {How to Avoid Potential Pitfalls in Recurrence Plot Based Data Analysis},
  author = {Marwan, Norbert},
  year = {2011},
  month = apr,
  volume = {21},
  pages = {1003--1017},
  publisher = {{World Scientific Publishing Co.}},
  issn = {0218-1274},
  doi = {10.1142/S0218127411029008},
  abstract = {Recurrence plots and recurrence quantification analysis have become popular in the last two decades. Recurrence based methods have on the one hand a deep foundation in the theory of dynamical systems and are on the other hand powerful tools for the investigation of a variety of problems. The increasing interest encompasses the growing risk of misuse and uncritical application of these methods. Therefore, we point out potential problems and pitfalls related to different aspects of the application of recurrence plots and recurrence quantification analysis.},
  file = {/home/aleksandar/Dropbox/Zotero/Marwan/Marwan_2011_How to avoid potential pitfalls in recurrence plot based data analysis.pdf;/home/aleksandar/Zotero/storage/NNGNI9BW/S0218127411029008.html;/home/aleksandar/Zotero/storage/ZGNWQMMS/S0218127411029008.html},
  journal = {International Journal of Bifurcation and Chaos},
  number = {04}
}

@article{maShortestPathTemporal2020,
  title = {Shortest Path of Temporal Networks: {{An}} Information Spreading Based Approach},
  shorttitle = {Shortest Path of Temporal Networks},
  author = {Ma, Yixin and Xue, Xiaoyu and Cai, Meng and Wang, Wei},
  year = {2020},
  issn = {1674-1056},
  doi = {10.1088/1674-1056/abb302},
  abstract = {The shortest path is a widely studied network science problem and has attracted great attention. Nevertheless, it draws little attention in temporal networks, in which temporal edges determine information dissemination. In this paper, we propose an information spreading based method to calculate the shortest paths distribution in temporal networks. We verify our method on both artificial and real-world temporal networks and obtain a good agreement. We further generalize our method to identify influential nodes and found an effective method. Finally, we verify the influential nodes identifying method on four networks.},
  journal = {Chinese Physics B},
  language = {en}
}

@article{maskarinecSpatialAnalysisCzech2017,
  title = {A {{Spatial}} Analysis of {{Czech}} Parliamentary Elections, 2006\textendash 2013},
  author = {Ma{\v s}karinec, Pavel},
  year = {2017},
  month = mar,
  volume = {69},
  pages = {426--457},
  issn = {0966-8136, 1465-3427},
  doi = {10.1080/09668136.2017.1313962},
  journal = {Europe-Asia Studies},
  language = {en},
  number = {3}
}

@article{maslovaSchwartzValueClusters2020,
  title = {Schwartz {{Value Clusters}} in {{Modern University Students}}},
  author = {Maslova, Olga V. and Shlyakhta, Dmitry A. and Yanitskiy, Mikhail S.},
  year = {2020},
  month = mar,
  volume = {10},
  pages = {66},
  issn = {2076-328X},
  doi = {10.3390/bs10030066},
  abstract = {People differ in their value hierarchies, i.e., in the importance they attach to basic personal values. A large number of studies were performed to establish similarities and differences between national, ethnic, or professional groups in terms of Schwartz's values structure. In addition to this sample-level approach, we found it useful to disclose a number of subgroups within those larger social groups, which are more homogeneous in themselves and reflect the individual-level types of personal values systems. The study was performed on university students (n = 1237) who were asked to fill in the SVS \cyrchar\cyri PVQ Schwartz's questionnaires. The sample was then treated with the K-means cluster analysis, which resulted in the division of the initial sample into three subgroups or clusters according to their values hierarchy being measured separately at the (1) Normative Ideals scale and (2) the scale of Behavioral Priorities. These clusters were equally common among male and female students, but they were unequally found in young people coming from different ethnic groups and regions, demonstrating the role of socio-cultural environment in building up personal values. The results may extend our capabilities for the prediction of the social, economic, and political behavior of the younger generation.},
  file = {/home/aleksandar/Dropbox/Zotero/Maslova et al/Maslova et al_2020_Schwartz Value Clusters in Modern University Students.pdf},
  journal = {Behavioral Sciences},
  language = {en},
  number = {3}
}

@article{masonIdeologuesIssuesPolarizing2018,
  title = {Ideologues without Issues: {{The}} Polarizing Consequences of Ideological Identities},
  shorttitle = {Ideologues without Issues},
  author = {Mason, Lilliana},
  year = {2018},
  volume = {82},
  pages = {866--887},
  publisher = {{Oxford University Press US}},
  journal = {Public Opinion Quarterly},
  number = {S1}
}

@article{maStatisticalInferenceApproach2018,
  title = {Statistical Inference Approach to Structural Reconstruction of Complex Networks from Binary Time Series},
  author = {Ma, Chuang and Chen, Han-Shuang and Lai, Ying-Cheng and Zhang, Hai-Feng},
  year = {2018},
  month = feb,
  volume = {97},
  pages = {022301},
  publisher = {{American Physical Society}},
  doi = {10.1103/PhysRevE.97.022301},
  abstract = {Complex networks hosting binary-state dynamics arise in a variety of contexts. In spite of previous works, to fully reconstruct the network structure from observed binary data remains challenging. We articulate a statistical inference based approach to this problem. In particular, exploiting the expectation-maximization (EM) algorithm, we develop a method to ascertain the neighbors of any node in the network based solely on binary data, thereby recovering the full topology of the network. A key ingredient of our method is the maximum-likelihood estimation of the probabilities associated with actual or nonexistent links, and we show that the EM algorithm can distinguish the two kinds of probability values without any ambiguity, insofar as the length of the available binary time series is reasonably long. Our method does not require any a priori knowledge of the detailed dynamical processes, is parameter-free, and is capable of accurate reconstruction even in the presence of noise. We demonstrate the method using combinations of distinct types of binary dynamical processes and network topologies, and provide a physical understanding of the underlying reconstruction mechanism. Our statistical inference based reconstruction method contributes an additional piece to the rapidly expanding ``toolbox'' of data based reverse engineering of complex networked systems.},
  file = {/home/aleksandar/Documents/Zotero/Ma et al/Ma et al_2018_Statistical inference approach to structural reconstruction of complex networks.pdf;/home/aleksandar/Zotero/storage/XYWAJ3RM/PhysRevE.97.html},
  journal = {Physical Review E},
  number = {2}
}

@article{mastroeniAgentBasedModelsOpinion2019,
  title = {Agent-{{Based Models}} for {{Opinion Formation}}: {{A Bibliographic Survey}}},
  shorttitle = {Agent-{{Based Models}} for {{Opinion Formation}}},
  author = {Mastroeni, Loretta and Vellucci, Pierluigi and Naldi, Maurizio},
  year = {2019},
  doi = {10.1109/ACCESS.2019.2913787},
  abstract = {Agent-based models are now largely adopted to describe how opinions emerge in a group of people. This survey provides an analysis of the literature on the subject, highlighting the major characteristics of such models. Over the last decade, the number of papers has grown at an overall annual rate of 16\%, though not continually. Two communities contribute to the research effort: physics and control systems. However, their mutual awareness and collaboration are rather low. The prevailing mechanism adopted to describe the interaction among the agents is bilateral, but not symmetric. In most cases, the opinion is described by a continuous variable. Just a few papers consider a utility function for the agents.},
  journal = {IEEE Access}
}

@article{mastroeniAgentBasedModelsOpinion2019a,
  title = {Agent-{{Based Models}} for {{Opinion Formation}}: {{A Bibliographic Survey}}},
  shorttitle = {Agent-{{Based Models}} for {{Opinion Formation}}},
  author = {Mastroeni, L. and Vellucci, P. and Naldi, M.},
  year = {2019},
  doi = {10.1109/ACCESS.2019.2913787},
  abstract = {Agent-based models are now largely adopted to describe how opinions emerge in a group of people. This survey provides an analysis of the literature on the subject, highlighting the major characteristics of such models. Over the last decade, the number of papers has grown at an overall annual rate of 16\%, though not continually. Two communities contribute to the research effort: physics and control systems. However, their mutual awareness and collaboration are rather low. The prevailing mechanism adopted to describe the interaction among the agents is bilateral, but not symmetric. In most cases, the opinion is described by a continuous variable. Just a few papers consider a utility function for the agents.},
  journal = {IEEE Access}
}

@article{mathewsWhyStudyComplexity1999,
  title = {Why {{Study}} the {{Complexity Sciences}} in the {{Social Sciences}}?},
  author = {Mathews, K. Michael and White, Michael C. and Long, Rebecca G.},
  year = {1999},
  month = apr,
  volume = {52},
  pages = {439--462},
  publisher = {{SAGE Publications Ltd}},
  issn = {0018-7267},
  doi = {10.1177/001872679905200402},
  abstract = {Over the past two decades, several new perspectives have emerged in the physical and natural sciences and are collectively referred to as the complexity sciences. Insights from these emerging perspectives have implications that merit consideration for developments and extensions of existing work at the metatheoretical, theoretical, and methodological levels in organization theory. The purpose of this manuscript is to: (a) provide an overview of the complexity sciences, (b) provide a justification and rationale for their inclusion into the social sciences, and (c) review the current organizational literature which utilizes and applies concepts from the complexity sciences to organizational phenomena.},
  file = {/home/aleksandar/Dropbox/Zotero/Mathews et al/Mathews et al_1999_Why Study the Complexity Sciences in the Social Sciences.pdf},
  journal = {Human Relations},
  keywords = {catastrophe theory,chaos theory,complexity,systems},
  language = {en},
  number = {4}
}

@article{matternasExaminingSupportAuthoritarian,
  title = {Examining {{Support}} for {{Authoritarian Alternatives}}: {{The Growing Populist Wave}} and {{Democratic Deconsolidation}} in {{Western European Regimes}}},
  author = {Matternas, Matthew and Witmer, Dr Richard},
  pages = {34},
  language = {en}
}

@article{mavrodievEnhancedDistortedWisdom2020,
  title = {Enhanced or Distorted Wisdom of Crowds? {{An}} Agent-Based Model of Opinion Formation under Social Influence},
  shorttitle = {Enhanced or Distorted Wisdom of Crowds?},
  author = {Mavrodiev, Pavlin and Schweitzer, Frank},
  year = {2020},
  month = aug,
  abstract = {We propose an agent-based model of collective opinion formation to study the wisdom of crowds under social influence. The opinion of an agent is a continuous positive value, denoting its subjective answer to a factual question. The wisdom of crowds states that the average of all opinions is close to the truth, i.e. the correct answer. But if agents have the chance to adjust their opinion in response to the opinions of others, this effect can be destroyed. Our model investigates this scenario by evaluating two competing effects: (i) agents tend to keep their own opinion (individual conviction \$\textbackslash beta\$), (ii) they tend to adjust their opinion if they have information about the opinions of others (social influence \$\textbackslash alpha\$). For the latter, two different regimes (full information vs. aggregated information) are compared. Our simulations show that social influence only in rare cases enhances the wisdom of crowds. Most often, we find that agents converge to a collective opinion that is even farther away from the true answer. So, under social influence the wisdom of crowds can be systematically wrong.},
  archivePrefix = {arXiv},
  eprint = {2008.10423},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Mavrodiev_Schweitzer/Mavrodiev_Schweitzer_2020_Enhanced or distorted wisdom of crowds.pdf;/home/aleksandar/Zotero/storage/56SAMARN/2008.html},
  journal = {arXiv:2008.10423 [nlin, physics:physics]},
  primaryClass = {nlin, physics:physics}
}

@article{mazzaroOrchestratingSilenceWinning2020,
  title = {Orchestrating Silence, Winning the Votes: {{Explaining}} Variation in Media Freedom during Elections},
  shorttitle = {Orchestrating Silence, Winning the Votes},
  author = {Mazzaro, Kyong},
  year = {2020},
  month = aug,
  volume = {66},
  pages = {102172},
  issn = {0261-3794},
  doi = {10.1016/j.electstud.2020.102172},
  abstract = {Restrictions on media freedom play a prominent role in the survival of incumbents in hybrid regimes. But while scholars agree that governments limit media freedom to punish opponents and mobilize supporters, less is known about the electoral incentives that explain the timing and location of restrictions. Using original microdata on government attempts to prevent the dissemination of information from Venezuela (2002\textendash 2015), I show that electoral competition at the local and national levels shape patterns of government-led restrictions on media freedom. At the local level, I find that differences in the opposition's strength incentivize governments to use restrictions to demobilize opponents where they are strongest, target marginal districts during local elections, and maintain dominance in their strongholds. At the national level, I show that as threats to the government party's primacy increase, so does the effect of local electoral competition on the hazard of restrictions.},
  file = {/home/aleksandar/Dropbox/Zotero/Mazzaro/Mazzaro_2020_Orchestrating silence, winning the votes.pdf;/home/aleksandar/Zotero/storage/VN5QJW2C/S0261379420300561.html},
  journal = {Electoral Studies},
  language = {en}
}

@article{mcavoyEvaluatingStructureCoefficient2020,
  title = {Evaluating the Structure Coefficient Theorem of Evolutionary Game Theory},
  author = {McAvoy, Alex and Wakeley, John},
  year = {2020},
  month = oct,
  abstract = {Evolutionary game theory is a foundational, if somewhat idealized, tool for modeling the natural selection of frequency-dependent traits in populations. In addition to modeling fecundity that can be influenced by the behaviors of others, this framework has also come to incorporate many varieties of demographic heterogeneity, most notably that of a population's spatial structure. Given the sophistication of current models of population dynamics, one of the most difficult obstacles lies in making analytical deductions, either qualitative or quantitative, about the long-term outcomes of an evolutionary process. The "structure coefficient theorem" is a well-known approach to this problem for mutation-selection processes under weak selection, but a general method of evaluating the terms it comprises is still lacking. Here we provide such a method for populations of fixed (but arbitrary) size and structure, using easily interpretable measures of population heterogeneity. This method encompasses a large family of evolutionary update mechanisms, and it includes complexity bounds in terms of the population size. We apply the method to the case of social goods produced and distributed among individuals in spatially-heterogeneous populations. We show that the outcome of selection varies dramatically depending on how goods are produced and distributed, on the details of population structure, and on the rate of mutation.},
  archivePrefix = {arXiv},
  eprint = {2010.14566},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/McAvoy_Wakeley/McAvoy_Wakeley_2020_Evaluating the structure coefficient theorem of evolutionary game theory.pdf;/home/aleksandar/Zotero/storage/R6QAFHC6/2010.html},
  journal = {arXiv:2010.14566 [q-bio]},
  keywords = {Quantitative Biology - Populations and Evolution},
  primaryClass = {q-bio}
}

@article{mccabeNetrdLibraryNetwork2020,
  title = {Netrd: {{A}} Library for Network Reconstruction and Graph Distances},
  shorttitle = {Netrd},
  author = {McCabe, Stefan and Torres, Leo and LaRock, Timothy and Haque, Syed Arefinul and Yang, Chia-Hung and Hartle, Harrison and Klein, Brennan},
  year = {2020},
  month = oct,
  abstract = {Over the last two decades, alongside the increased availability of large network datasets, we have witnessed the rapid rise of network science. For many systems, however, the data we have access to is not a direct description of the underlying network. More and more, we see the drive to study networks that have been inferred or reconstructed from non-network data---in particular, using time series data from the nodes in a system to infer likely connections between them. Selecting the most appropriate technique for this task is a challenging problem in network science. Different reconstruction techniques usually have different assumptions, and their performance varies from system to system in the real world. One way around this problem could be to use several different reconstruction techniques and compare the resulting networks. However, network comparison is also not an easy problem, as it is not obvious how best to quantify the differences between two networks, in part because of the diversity of tools for doing so. The netrd Python package seeks to address these two parallel problems in network science by providing, to our knowledge, the most extensive collection of both network reconstruction techniques and network comparison techniques (often referred to as graph distances) in a single library (https://github.com/netsiphd/netrd). In this article, we detail the two main functionalities of the netrd package. Along the way, we describe some of its other useful features. This package builds on commonly used Python packages and is already a widely used resource for network scientists and other multidisciplinary researchers. With ongoing open-source development, we see this as a tool that will continue to be used by all sorts of researchers to come.},
  archivePrefix = {arXiv},
  eprint = {2010.16019},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/McCabe et al/McCabe et al_2020_netrd.pdf;/home/aleksandar/Zotero/storage/EJ37WCEI/2010.html},
  journal = {arXiv:2010.16019 [physics]},
  keywords = {Computer Science - Social and Information Networks,Physics - Physics and Society},
  primaryClass = {physics}
}

@article{mccaffreeIntegrativeSociologicalTheory2019,
  title = {Towards an Integrative Sociological Theory of Empathy},
  author = {McCaffree, Kevin},
  year = {2019},
  month = nov,
  pages = {1368431019890494},
  issn = {1368-4310},
  doi = {10.1177/1368431019890494},
  abstract = {Sociological theories of morality have grown in prevalence over the last decade and a half. These theories often focus on developing single concepts such as identity, reputation or emotion, or they provide sweeping historical accounts. Such theories often also take the construct of empathy for granted, as an inevitable consequence of morality. Here, I present a mechanistic theory of empathy which operates at multiple levels of analysis. The approach offered can help frame particular topics in the sociology of morality, though it is also robust enough to analyse historical change. Specifically, the theory presented here is composed of sets of situational, structural and societal mechanisms. Each set is discussed, as well as their relation to one another, before turning to an explication of how the present theory relates to a selective sampling of prominent theories in the contemporary sociology of morality.},
  journal = {European Journal of Social Theory},
  language = {en}
}

@article{mcconvilleModelAssistedSurveyRegression2017,
  title = {Model-{{Assisted Survey Regression Estimation}} with the {{Lasso}}},
  author = {McConville, Kelly S. and Breidt, F. Jay and Lee, Thomas C. M. and Moisen, Gretchen G.},
  year = {2017},
  month = jun,
  volume = {5},
  pages = {131--158},
  issn = {2325-0984},
  doi = {10.1093/jssam/smw041},
  abstract = {Abstract.  In the U.S. Forest Service's Forest Inventory and Analysis (FIA) program, as in other natural resource surveys, many auxiliary variables are availabl},
  file = {/home/aleksandar/Zotero/storage/ULX7JIZM/3775768.html},
  journal = {Journal of Survey Statistics and Methodology},
  language = {en},
  number = {2}
}

@article{mccraeValidationFivefactorModel1987,
  title = {Validation of the Five-Factor Model of Personality across Instruments and Observers.},
  author = {McCrae, Robert R. and Costa, Paul T.},
  year = {1987},
  volume = {52},
  pages = {81--90},
  issn = {1939-1315, 0022-3514},
  doi = {10.1037/0022-3514.52.1.81},
  journal = {Journal of Personality and Social Psychology},
  language = {en},
  number = {1}
}

@article{mccraeWhatPersonalityScales2019,
  title = {What {{Personality Scales Measure}}: {{A New Psychometrics}} and {{Its Implications}} for {{Theory}} and {{Assessment}}},
  shorttitle = {What {{Personality Scales Measure}}},
  author = {McCrae, Robert R. and M{\~o}ttus, Ren{\'e}},
  year = {2019},
  month = aug,
  volume = {28},
  pages = {415--420},
  issn = {0963-7214, 1467-8721},
  doi = {10.1177/0963721419849559},
  abstract = {Classical psychometrics held that scores on a personality measure were determined by the trait assessed and random measurement error. A new view proposes a much richer and more complex model that includes trait variance at multiple levels of a hierarchy of traits and systematic biases shaped by the implicit personality theory of the respondent. The model has implications for the optimal length and content of scales and for the use of scales intended to correct for evaluative bias; further, it suggests that personality assessments should supplement self-reports with informant ratings. The model also has implications for the very nature of personality traits.},
  file = {/home/aleksandar/Zotero/storage/VSRQKASB/McCrae_Mõttus_2019_What Personality Scales Measure.pdf},
  journal = {Current Directions in Psychological Science},
  language = {en},
  number = {4}
}

@article{mcelwainNationalizationJapaneseElections2012,
  title = {The Nationalization of {{Japanese}} Elections},
  author = {McElwain, Kenneth Mori},
  year = {2012},
  month = sep,
  volume = {12},
  pages = {323--350},
  issn = {1598-2408, 2234-6643},
  doi = {10.1017/S1598240800008055},
  journal = {Journal of East Asian Studies},
  language = {en},
  number = {03}
}

@article{mcfarlandEffectsUsingPartial2020,
  ids = {mcfarlandEffectsUsingPartial2020a},
  title = {The {{Effects}} of {{Using Partial}} or {{Uncorrected Correlation Matrices When Comparing Network}} and {{Latent Variable Models}}},
  author = {McFarland, Dennis},
  year = {2020},
  month = feb,
  volume = {8},
  issn = {2079-3200},
  doi = {10.3390/jintelligence8010007},
  abstract = {Network models of the WAIS-IV based on regularized partial correlation matrices have been reported to outperform latent variable models based on uncorrected correlation matrices. The present study sought to compare network and latent variable models using both partial and uncorrected correlation matrices with both types of models. The results show that a network model provided better fit to matrices of partial correlations but latent variable models provided better fit to matrices of full correlations. This result is due to the fact that the use of partial correlations removes most of the covariance common to WAIS-IV tests. Modeling should be based on uncorrected correlations since these represent the majority of shared variance between WAIS-IV test scores.},
  journal = {Journal of Intelligence},
  language = {eng},
  number = {1},
  pmid = {32075306}
}

@article{mcmillanDyadsTriadsComparison2020,
  title = {Beyond {{Dyads}} and {{Triads}}: {{A Comparison}} of {{Tetrads}} in {{Twenty Social Networks}}},
  shorttitle = {Beyond {{Dyads}} and {{Triads}}},
  author = {McMillan, Cassie and Felmlee, Diane},
  year = {2020},
  month = oct,
  pages = {0190272520944151},
  publisher = {{SAGE Publications Inc}},
  issn = {0190-2725},
  doi = {10.1177/0190272520944151},
  abstract = {Social psychologists focus on the microlevel features that define interaction, often attending to dyads and triads. We argue that there also is utility in studying how configurations of four actors, or tetrads, pattern our social world. The current project considers the prevalence of directed tetrads across twenty social networks representing five relationship types (friendship, legislative co-sponsorship, Twitter, advice seeking, and email). By comparing these observed networks to randomly generated conditional networks, we identify tetrads that occur more frequently than expected, or network motifs. In all twenty networks, we find evidence for six tetrad motifs that collectively highlight tendencies toward hierarchy, clustering, and bridging in social interaction. Variations across network genres also emerge, suggesting that unique tetrad structural signatures could define different types of interaction.},
  journal = {Social Psychology Quarterly},
  language = {en}
}

@article{mcneishSpecifyingLocationScaleModels2020,
  title = {Specifying {{Location}}-{{Scale Models}} for {{Heterogeneous Variances}} as {{Multilevel SEMs}}},
  author = {McNeish, Daniel},
  year = {2020},
  month = mar,
  pages = {1094428120913083},
  publisher = {{SAGE Publications Inc}},
  issn = {1094-4281},
  doi = {10.1177/1094428120913083},
  abstract = {Standard multilevel models focus on variables that predict the mean while the within-group variability is largely treated as a nuisance. Recent work has shown the advantage of including predictors for both the mean (the location submodel) and the variability (the scale submodel) within a single model. Constrained versions of the model can be fit in standard mixed effect model software, but the most general version with random effects in each of the location and scale submodels has been noted for being difficult to fit and estimate in software. However, the latest release of Mplus includes new capabilities that facilitate fitting the general version of the model as a multilevel structural equation model (SEM). This article introduces the general form of the model that includes location and scale random effects (called the location-scale model) and notes how it can be envisioned as a multilevel SEM. We provide a tutorial with example analyses and Mplus code for the model with two-level cross-sectional data and three-level repeated measures data and discuss how such a model has potential to extend recent developments in organizational science.},
  file = {/home/aleksandar/Dropbox/Zotero/McNeish/McNeish_2020_Specifying Location-Scale Models for Heterogeneous Variances as Multilevel SEMs.pdf},
  journal = {Organizational Research Methods},
  language = {en}
}

@article{mcphersonBirdsFeatherHomophily2001,
  title = {Birds of a {{Feather}}: {{Homophily}} in {{Social Networks}}},
  shorttitle = {Birds of a {{Feather}}},
  author = {McPherson, Miller and {Smith-Lovin}, Lynn and Cook, James M},
  year = {2001},
  month = aug,
  volume = {27},
  pages = {415--444},
  issn = {0360-0572, 1545-2115},
  doi = {10.1146/annurev.soc.27.1.415},
  abstract = {Scientists working with observational data often need to know attributes of nodes in social networks. When these attributes are partly missing, collective classification can be used to infer them. However, factors affecting its performance are not yet well understood, nor the conditions that bias the performance against minorities. To this end, we systematically study how structural properties of the network and the training sample influence the performance of collective classification. Our main finding shows that mean classification performance can empirically and analytically be predicted by structural properties such as homophily, fraction of minorities, and edge density. Our results establish evaluation benchmarks, especially helpful when no ground-truth is available.},
  file = {/home/aleksandar/Zotero/storage/76PSKX9Y/McPherson et al. - 2001 - Birds of a Feather Homophily in Social Networks.pdf},
  journal = {Annual Review of Sociology},
  language = {en},
  number = {1}
}

@article{mcphieOriginAlkalineInactivation1975,
  title = {The Origin of the Alkaline Inactivation of Pepsinogen},
  author = {McPhie, P.},
  year = {1975},
  month = dec,
  volume = {14},
  pages = {5253--5256},
  issn = {0006-2960},
  abstract = {Above pH 8.5, pepsinogen is converted into a form which cannot be activated to pepsin on exposure to low pH. Intermediate exposure to neutral pH, however, returns the protein to a form which can be activated. Evidence is presented for a reversible, small conformational change in the molecule, distinct from the unfolding of the protein. At the same time, the molecule is converted to a form of limited solubility, which is precipitated at low pH, where activation is normally seen. The results are interpreted in terms of the peculiar structure of the pepsinogen molecule. Titration of the basic NH2-terminal region produced an open form, which can return to the native form at neutral pH, but which is maintained at low pH by neutralization of carboxylate groups in the pepsin portion.},
  journal = {Biochemistry},
  language = {eng},
  number = {24},
  pmid = {44}
}

@article{mcquillanCulturalConvergenceInsights2020,
  title = {Cultural {{Convergence}}: {{Insights}} into the Behavior of Misinformation Networks on {{Twitter}}},
  shorttitle = {Cultural {{Convergence}}},
  author = {McQuillan, Liz and McAweeney, Erin and Bargar, Alicia and Ruch, Alex},
  year = {2020},
  month = jul,
  abstract = {How can the birth and evolution of ideas and communities in a network be studied over time? We use a multimodal pipeline, consisting of network mapping, topic modeling, bridging centrality, and divergence to analyze Twitter data surrounding the COVID-19 pandemic. We use network mapping to detect accounts creating content surrounding COVID-19, then Latent Dirichlet Allocation to extract topics, and bridging centrality to identify topical and non-topical bridges, before examining the distribution of each topic and bridge over time and applying Jensen-Shannon divergence of topic distributions to show communities that are converging in their topical narratives.},
  archivePrefix = {arXiv},
  eprint = {2007.03443},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Dropbox/Zotero/McQuillan et al/McQuillan et al_2020_Cultural Convergence.pdf;/home/aleksandar/Zotero/storage/2EPR2CLA/2007.html},
  journal = {arXiv:2007.03443 [physics]},
  primaryClass = {physics}
}

@article{meansPermutationMethodNetwork2020,
  title = {A Permutation Method for Network Assembly},
  author = {Means, Shawn A. and Bl{\"a}sche, Christian and Laing, Carlo R.},
  year = {2020},
  month = oct,
  volume = {15},
  pages = {e0240888},
  publisher = {{Public Library of Science}},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0240888},
  abstract = {We present a method for assembling directed networks given a prescribed bi-degree (in- and out-degree) sequence. This method utilises permutations of initial adjacency matrix assemblies that conform to the prescribed in-degree sequence, yet violate the given out-degree sequence. It combines directed edge-swapping and constrained Monte-Carlo edge-mixing for improving approximations to the given out-degree sequence until it is exactly matched. Our method permits inclusion or exclusion of `multi-edges', allowing assembly of weighted or binary networks. It further allows prescribing the overall percentage of such multiple connections\textemdash permitting exploration of a weighted synthetic network space unlike any other method currently available for comparison of real-world networks with controlled multi-edge proportion null spaces. The graph space is sampled by the method non-uniformly, yet the algorithm provides weightings for the sample space across all possible realisations allowing computation of statistical averages of network metrics as if they were sampled uniformly. Given a sequence of in- and out- degrees, the method can also produce simple graphs for sequences that satisfy conditions of graphicality. Our method successfully builds networks with order O(107) edges on the scale of minutes with a laptop running Matlab. We provide our implementation of the method on the GitHub repository for immediate use by the research community, and demonstrate its application to three real-world networks for null-space comparisons as well as the study of dynamics of neuronal networks.},
  file = {/home/aleksandar/Documents/Zotero/Means et al/Means et al_2020_A permutation method for network assembly.pdf;/home/aleksandar/Zotero/storage/HYGTEKFG/article.html;/home/aleksandar/Zotero/storage/Q5U6UA6N/article.html},
  journal = {PLOS ONE},
  keywords = {Cognitive science,Macaque,Metabolic networks,Network analysis,Neural networks,Neurons,Operator theory,Permutation},
  language = {en},
  number = {10}
}

@article{medina-lopezCharacterizingHeadDegree2020,
  title = {Characterizing the Head of the Degree Distributions of Growing Networks},
  author = {{Medina-L{\'o}pez}, Jan and Finke, Jorge},
  year = {2020},
  month = nov,
  abstract = {The analysis in this paper helps to explain the formation of growing networks with degree distributions that follow extended exponential or power-law tails. We present a generic model in which edge dynamics are driven by a continuous attachment of new nodes and a mixed attachment mechanism that triggers random or preferential attachment. Furthermore, reciprocal edges to newly added nodes are established according to a response mechanism. The proposed framework extends previous mixed attachment models by allowing the number of new edges to vary according to various discrete probability distributions, including Poisson, Binomial, Zeta, and Log-Series. We derive analytical expressions for the limit in-degree distribution that results from the mixed attachment and response mechanisms. Moreover, we describe the evolution of the dynamics of the cumulative in-degree distribution. Simulation results illustrate how the number of new edges and the process of reciprocity significantly impact the head of the degree distribution.},
  archivePrefix = {arXiv},
  eprint = {2011.04643},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Zotero/storage/UJY2DCHP/Medina-López and Finke - 2020 - Characterizing the head of the degree distribution.pdf},
  journal = {arXiv:2011.04643 [physics, stat]},
  keywords = {Mathematics - Probability,Physics - Physics and Society,Statistics - Applications},
  language = {en},
  primaryClass = {physics, stat}
}

@inproceedings{medinaserranoDancingPartisanBeat2020,
  title = {Dancing to the {{Partisan Beat}}: {{A First Analysis}} of {{Political Communication}} on {{TikTok}}},
  shorttitle = {Dancing to the {{Partisan Beat}}},
  booktitle = {12th {{ACM Conference}} on {{Web Science}}},
  author = {Medina Serrano, Juan Carlos and Papakyriakopoulos, Orestis and Hegelich, Simon},
  year = {2020},
  month = jul,
  pages = {257--266},
  publisher = {{Association for Computing Machinery}},
  address = {{Southampton, United Kingdom}},
  doi = {10.1145/3394231.3397916},
  abstract = {TikTok is a video-sharing social networking service, whose popularity is increasing rapidly. It was the world's second-most downloaded app in 2019. Although the platform is known for having users posting videos of themselves dancing, lip-syncing, or showcasing other talents, user-videos expressing political views have seen a recent spurt. This study aims to perform a primary evaluation of political communication on TikTok. We collect a set of US partisan Republican and Democratic videos to investigate how users communicated with each other about political issues. With the help of computer vision, natural language processing, and statistical tools, we illustrate that political communication on TikTok is much more interactive in comparison to other social media platforms, with users combining multiple information channels to spread their messages. We show that political communication takes place in the form of communication trees since users generate branches of responses to existing content. In terms of user demographics, we find that users belonging to both the US parties are young and behave similarly on the platform. However, Republican users generated more political content and their videos received more responses; on the other hand, Democratic users engaged significantly more in cross-partisan discussions.},
  isbn = {978-1-4503-7989-2},
  series = {{{WebSci}} '20}
}

@article{medoFragilityOpinionFormation2020,
  title = {The Fragility of Opinion Formation in a Complex World},
  author = {Medo, Mat{\'u}{\v s} and Mariani, Manuel S. and L{\"u}, Linyuan},
  year = {2020},
  month = oct,
  abstract = {With vast amounts of high-quality information at our fingertips, how is it possible that many people believe that the Earth is flat and vaccination harmful? Motivated by this question, we quantify the implications of an opinion formation mechanism whereby an uninformed observer gradually forms opinions about a world composed of subjects interrelated by a signed network of mutual trust and distrust. We show numerically and analytically that the observer's resulting opinions are highly inconsistent (they tend to be independent of the observer's initial opinions) and unstable (they exhibit wide stochastic variations). Opinion inconsistency and instability increase with the world complexity represented by the number of subjects, which can be prevented by suitably expanding the observer's initial amount of information. Our findings imply that even an individual who initially trusts credible information sources may end up trusting the deceptive ones if at least a small number of trust relations exist between the credible and deceptive sources.},
  archivePrefix = {arXiv},
  eprint = {2010.12355},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Zotero/storage/LATL4P67/Medo et al_2020_The fragility of opinion formation in a complex world.pdf;/home/aleksandar/Zotero/storage/GLHDZIFF/2010.html},
  journal = {arXiv:2010.12355 [cond-mat, physics:physics]},
  keywords = {Computer Science - Social and Information Networks,Condensed Matter - Statistical Mechanics,Physics - Physics and Society},
  primaryClass = {cond-mat, physics:physics}
}

@article{meeusenFacilitatorsIntergenerationalSimilarity2020,
  title = {Facilitators of Intergenerational Similarity in Social and Political Attitudes: The Role of Discussion, Sophistication, Attitudinal Homogeneity, and Gender},
  shorttitle = {Facilitators of Intergenerational Similarity in Social and Political Attitudes},
  author = {Meeusen, Cecil and Boonen, Joris},
  year = {2020},
  month = nov,
  issn = {1741-1416},
  doi = {10.1057/s41269-020-00186-0},
  abstract = {While parents remain key socializing agents for political attitudes of adolescents, we observe that parent\textendash child similarity in these attitudes is less substantial than initially theorized and seems to vary across studies and type of~attitude. We hypothesize that social learning mechanisms explain differences in the strength of parent\textendash child similarity. To assess this proposition, we compare the moderating power of political discussion, political sophistication, parental homogeneity, and gender using data from the Belgian~Parent\textendash Child Socialization Study 2013 (N\,=\,1943 families). Our results demonstrate that parents and children resemble in their political and social attitudes and that this similarity is stronger for concrete and socially salient attitudes and reinforced by frequency in political discussion. Results also~indicate that social learning practices, or at least the ones considered in this study, are not necessary conditions (anymore) for parent\textendash child correspondence. Frequency of cue-giving in the form of direct communication is important to internalize parental~attitudes but political sophistication and consistency in cue-giving less so. Parents and children correspond even in the absence of optimal social learning conditions which suggests that informal learning and observation of parental behavior are crucial to consider as well.},
  journal = {Acta Politica},
  language = {en}
}

@article{meijersMeasuringPopulismPolitical,
  title = {Measuring {{Populism}} in {{Political Parties}}: {{Appraisal}} of a {{New Approach}}},
  author = {Meijers, Maurits J and Zaslove, Andrej},
  pages = {47},
  abstract = {Populism has become a pervasive concept in political science research. However, a central and basic question remains unanswered: which European parties are more populist than others? Despite the increasing wealth of studies on populism in parties, we lack data that measures populism in political parties in a valid and precise manner, that recognizes that populism is constituted by multiple dimensions, and that ensures full coverage of all parties in Europe. In this article, we first appraise the weaknesses of existing approaches. Arguing that parties' populism should be measured as a latent construct, we then advocate a new approach to operationalizing and measuring populism in political parties using expert surveys. Relying on the Populism and Political Parties Expert Survey spanning 250 political parties in 28 European countries, we show that populism is best measured in a multidimensional and continuous manner. We subsequently illustrate the advantages of our approach for empirical analysis in political science.},
  file = {/home/aleksandar/Dropbox/Zotero/Meijers_Zaslove/Meijers_Zaslove_Measuring Populism in Political Parties.pdf},
  language = {en}
}

@article{meijersMeasuringPopulismPolitical2020,
  title = {Measuring {{Populism}} in {{Political Parties}}: {{Appraisal}} of a {{New Approach}}},
  shorttitle = {Measuring {{Populism}} in {{Political Parties}}},
  author = {Meijers, Maurits J. and Zaslove, Andrej},
  year = {2020},
  month = jul,
  pages = {001041402093808},
  issn = {0010-4140, 1552-3829},
  doi = {10.1177/0010414020938081},
  abstract = {Populism has become a pervasive concept in political science research. However, a central and basic question remains unanswered: which European parties are more populist than others? Despite the increasing wealth of studies on populism in parties, we lack data that measures populism in political parties in a valid and precise manner, that recognizes that populism is constituted by multiple dimensions, and that ensures full coverage of all parties in Europe. In this article, we first appraise the weaknesses of existing approaches. Arguing that parties' populism should be measured as a latent construct, we then advocate a new approach to operationalizing and measuring populism in political parties using expert surveys. Relying on the Populism and Political Parties Expert Survey spanning 250 political parties in 28 European countries, we show that populism is best measured in a multi-dimensional and continuous manner. We subsequently illustrate the advantages of our approach for empirical analysis in political science.},
  file = {/home/aleksandar/Zotero/storage/T7AIIRB2/Meijers and Zaslove - 2020 - Measuring Populism in Political Parties Appraisal.pdf},
  journal = {Comparative Political Studies},
  language = {en}
}

@article{meleshevichGeographicalPatternsParty2006,
  title = {Geographical Patterns of Party Support in the {{Baltic States}}, {{Russia}}, and {{Ukraine}}},
  author = {Meleshevich, Andrey},
  year = {2006},
  month = apr,
  volume = {13},
  pages = {113--129},
  issn = {0969-7764, 1461-7145},
  doi = {10.1177/0969776406062522},
  journal = {European Urban and Regional Studies},
  language = {en},
  number = {2}
}

@article{melguizoDesacuerdoModelosFormacion2020,
  title = {El Desacuerdo En Modelos de Formaci\'on de Opiniones: Resultados de Est\'atica Comparativa},
  shorttitle = {El Desacuerdo En Modelos de Formaci\'on de Opiniones},
  author = {Melguizo, Isabel},
  year = {2020},
  month = jun,
  volume = {23},
  issn = {2145-454X, 0123-5362},
  doi = {10.12804/revistas.urosario.edu.co/economia/a.9112},
  abstract = {This paper further studies the persistence of disagreement in a model similar to Melguizo (2019), by relaxing two essential assumptions. First, attitudes are random variables, and second, individuals may be homophilous at different extents. Regarding the first extension, the finding is that disagreement persists with the highest probability across the attribute that exhibits the highest mean of the distribution of initial differences in average attitudes. Regarding the second one, the finding is that the magnitude of disagreement and the speed of convergence to it, increase with respect to the case in which individuals are homophilous to the same extent.},
  file = {/home/aleksandar/Zotero/storage/6QKWMI8P/Melguizo - 2020 - El desacuerdo en modelos de formación de opiniones.pdf},
  journal = {Revista de Econom\'ia del Rosario},
  language = {en},
  number = {2}
}

@inproceedings{melnikovDynamicInteractionBasedReputation2018,
  title = {Towards {{Dynamic Interaction}}-{{Based Reputation Models}}},
  booktitle = {2018 {{IEEE}} 32nd {{International Conference}} on {{Advanced Information Networking}} and {{Applications}} ({{AINA}})},
  author = {Melnikov, A. and Lee, J. and Rivera, V. and Mazzara, M. and Longo, L.},
  year = {2018},
  month = may,
  pages = {422--428},
  issn = {2332-5658},
  doi = {10.1109/AINA.2018.00070},
  abstract = {In this paper, we investigate how dynamic properties of reputation can influence the quality of users' ranking. Reputation systems should be based on rules that can guarantee high level of trust and help identify unreliable units. To understand the effectiveness of dynamic properties in the evaluation of reputation, we propose our own model (DIB-RM) that utilizes three factors: forgetting, cumulative, and activity period. In order to evaluate the model, we use data from StackOverflow which also has its own reputation model. We estimate similarity of ratings between DIB-RM and the StackOverflow reputation model to test our hypothesis. We use two values to calculate our metrics: DIB-RM reputation and historical reputation. We found out that historical reputation gives better metric values. Our preliminary results are presented for different sets of values of the aforementioned factors in order to analyze how effectively the model can be used for modeling reputation systems.},
  file = {/home/aleksandar/Zotero/storage/RMJAAYV5/Melnikov et al_2018_Towards Dynamic Interaction-Based Reputation Models.pdf;/home/aleksandar/Zotero/storage/Z3EESJBS/8432272.html},
  keywords = {Computational modeling,cumulative activity period,DIB-RM reputation,DONE,dynamic interaction-based reputation models,dynamic model,dynamic properties,Electronic mail,forgetting activity period,historical reputation,information retrieval,Integrated circuits,Internet,Load modeling,Measurement,modeling reputation systems,reputation,Social network services,social networks,StackOverflow reputation model,trust management,Web sites}
}

@article{melvilleTrajectoriesRegimeTransformation2013,
  title = {Trajectories of Regime Transformation and Types of Stateness in Post-Communist Countries},
  author = {Melville, Andrei and Stukal, Denis and Mironyuk, Mikhail},
  year = {2013},
  month = dec,
  volume = {14},
  pages = {431--459},
  issn = {1570-5854, 1568-0258},
  doi = {10.1080/15705854.2013.772749},
  journal = {Perspectives on European Politics and Society},
  language = {en},
  number = {4}
}

@book{menczerFirstCourseNetwork2020,
  title = {A First Course in Network Science.},
  author = {Menczer, Filippo and Fortunato, Santo and Davis, Clayton A},
  year = {2020},
  publisher = {{Cambridge University Press}},
  address = {{Cambridge}},
  abstract = {Networks are everywhere: networks of friends, transportation networks and the Web. Neurons in our brains and proteins within our bodies form networks that determine our intelligence and survival. This modern, accessible textbook introduces the basics of network science for a wide range of job sectors from management to marketing, from biology to engineering, and from neuroscience to the social sciences. Students will develop important, practical skills and learn to write code for using networks in their areas of interest - even as they are just learning to program with Python. Extensive sets of tutorials and homework problems provide plenty of hands-on practice and longer programming tutorials online further enhance students' programming skills. This intuitive and direct approach makes the book ideal for a first course, aimed at a wide audience without a strong background in mathematics or computing but with a desire to learn the fundamentals and applications of network science.},
  annotation = {OCLC: 1139351272},
  language = {English}
}

@book{menezesComplexNetworks2013,
  title = {Complex {{Networks}}},
  editor = {Menezes, Ronaldo and Evsukoff, Alexandre and Gonz{\'a}lez, Marta C.},
  year = {2013},
  volume = {424},
  publisher = {{Springer Berlin Heidelberg}},
  address = {{Berlin, Heidelberg}},
  doi = {10.1007/978-3-642-30287-9},
  isbn = {978-3-642-30286-2 978-3-642-30287-9},
  language = {en},
  series = {Studies in {{Computational Intelligence}}}
}

@article{meninAmountInformationMeasurement2020,
  title = {Amount of {{Information}} and {{Measurement Uncertainty}}},
  author = {Menin, Boris},
  year = {2020},
  month = may,
  pages = {1--8},
  issn = {2348-0130},
  doi = {10.9734/psij/2020/v24i330179},
  abstract = {Methodology: Using the information approach and calculating the amount of information contained in the model of measuring a physical constant, we formulate a quantitative indicator for analyzing the results of the experiment. Results: The appropriateness of applying the described approach is checked when studying the database when measuring various physical constants. The approach is applicable to the analysis of results obtained both for a long and a short period of time. Conclusion: The information-theoretical approach allows us to formulate a universal indicator of the threshold mismatch between the model and the phenomenon, applicable to all scientific and technical fields in which the International System of Units (SI) is used.},
  file = {/home/aleksandar/Dropbox/Zotero/Menin/Menin_2020_Amount of Information and Measurement Uncertainty.pdf},
  journal = {Physical Science International Journal},
  language = {en}
}

@inproceedings{mensahCharacterizingEvolutionCommunities2020,
  title = {Characterizing the {{Evolution}} of {{Communities}} on {{Reddit}}},
  booktitle = {International {{Conference}} on {{Social Media}} and {{Society}}},
  author = {Mensah, Humphrey and Xiao, Lu and Soundarajan, Sucheta},
  year = {2020},
  month = jul,
  pages = {58--64},
  publisher = {{Association for Computing Machinery}},
  address = {{Toronto, ON, Canada}},
  doi = {10.1145/3400806.3400814},
  abstract = {One of the most important structures in social networks is communities. Communities in social networks evolve over time. Understanding the evolution of communities is useful in many applications, such as building successful communities, maintaining the success of communities, etc. There have been some works on studying online communities such as understanding the life cycle of users and understanding the loyalty of members in a community. An aspect of online community studies that has not been sufficiently studied is the evolution of communities over time. In this work, we investigate factors that significantly differentiate the different parts of the evolution of communities. Firstly, we identify the different patterns that can exist in the evolution of communities. Next, we examine how different features differentiate parts of the patterns identified. Experimental results showed that the linguistic style of users who make posts and the interaction dynamics of members in a community are related to different parts of communities' evolution with respect to the number of active users.},
  isbn = {978-1-4503-7688-4},
  keywords = {TODO},
  series = {{{SMSociety}}'20}
}

@article{merkleBlavaanBayesianStructural2018,
  title = {Blavaan: {{Bayesian}} Structural Equation Models via Parameter Expansion},
  shorttitle = {Blavaan},
  author = {Merkle, Edgar C. and Rosseel, Yves},
  year = {2018},
  volume = {85},
  issn = {1548-7660},
  doi = {10.18637/jss.v085.i04},
  abstract = {This article describes blavaan, an R package for estimating Bayesian structural equation models (SEMs) via JAGS and for summarizing the results. It also describes a novel parameter expansion approach for estimating specific types of models with residual covariances, which facilitates estimation of these models in JAGS. The methodology and software are intended to provide users with a general means of estimating Bayesian SEMs, both classical and novel, in a straightforward fashion. Users can estimate Bayesian versions of classical SEMs with lavaan syntax, they can obtain state-of-the-art Bayesian fit measures associated with the models, and they can export JAGS code to modify the SEMs as desired. These features and more are illustrated by example, and the parameter expansion approach is explained in detail.},
  archivePrefix = {arXiv},
  eprint = {1511.05604},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Zotero/storage/RGNBGN83/1511.html},
  journal = {Journal of Statistical Software},
  number = {4}
}

@article{mesnardsDetectingBotsAssessing,
  ids = {mesnardsDetectingBotsAssessinga,mesnardsDetectingBotsAssessingb},
  title = {Detecting {{Bots}} and {{Assessing Their Impact}} in {{Social Networks}}. ({{arXiv}}:1810.12398v3 [Cs.{{SI}}] {{UPDATED}})},
  author = {des Mesnards, Nicolas Guenon and Hunter, David Scott and el Hjouji, Zakaria and Zaman, Tauhid},
  doi = {arXiv:1810.12398v3},
  abstract = {{$<$}p{$>$}Online social networks are often subject to influence campaigns by malicious actors through the use of automated accounts known as bots. We consider the problem of detecting bots in online social networks and assessing their impact on the opinions of individuals. We begin by analyzing the behavior of bots in social networks and identify that they exhibit heterophily, meaning they interact with humans more than other bots. We use this property to develop a detection algorithm based on the Ising model from statistical physics. The bots are identified by solving a minimum cut problem. We show that this Ising model algorithm can identify bots with higher accuracy while utilizing much less data than other state of the art methods. {$<$}/p{$>$} {$<$}p{$>$}We then develop a a function we call generalized harmonic influence centrality to estimate the impact bots have on the opinions of users in social networks. This function is based on a generalized opinion dynamics model and captures how the activity level and network connectivity of the bots shift equilibrium opinions. To apply generalized harmonic influence centrality to real social networks, we develop a deep neural network to measure the opinions of users based on their social network posts. Using this neural network, we then calculate the generalized harmonic influence centrality of bots in multiple real social networks. For some networks we find that a limited number of bots can cause non-trivial shifts in the population opinions. In other networks, we find that the bots have little impact. Overall we find that generalized harmonic influence centrality is a useful operational tool to measure the impact of bots in social networks. {$<$}/p{$>$}},
  journal = {arXiv Computer Science}
}

@techreport{mesoudiCulturalEvolutionFootball2019,
  title = {Cultural Evolution of Football Tactics: {{Strategic}} Social Learning in Managers' Choice of Formation},
  shorttitle = {Cultural Evolution of Football Tactics},
  author = {Mesoudi, Alex},
  year = {2019},
  month = nov,
  institution = {{SocArXiv}},
  doi = {10.31235/osf.io/wrx9f},
  abstract = {In order to solve complex problems or make difficult decisions, people must strategically combine personal information acquired directly from experience (individual learning) and social information copied from others (social learning). The game of football (soccer) provides a wealth of real world data with which to quantify managers' use of personal and social information in selecting team formations. I analyse a 5-year dataset of all games (n=9127, 2012-2017) in the five top European leagues (English Premier League, German Bundesliga, Spanish La Liga, French Ligue 1 and Italian Serie A) to quantify the extent to which a manager's initial match formation is guided by their personal past use or success with that formation, or other managers' use or success with that formation. I focus in particular on the use of the 4231 formation, which was the dominant formation at the start of this period but showed a gradual decline in use over time. I find that, as predicted, a manager's choice of whether to use 4231 or not is influenced by both their recent use of 4231 (personal information) and the use of 4231 in the entire population of managers in that division (social information). Contrary to expectations, managers appeared to rely more on personal than social information, although this estimate was highly variable across managers and divisions. Serie A managers, in particular, showed a much stronger use of personal information, likely due to the rarity of 4231 in that division. Finally, there did not appear to be an adaptive tradeoff between social and personal information use, with the relative reliance on each failing to predict managerial success.},
  language = {en},
  type = {Preprint}
}

@incollection{messnerSpatialAnalysesHomicide2004,
  title = {Spatial Analyses of Homicide with Areal Data},
  booktitle = {Spatially Integrated Social Science},
  author = {Messner, S. and Anselin, Luc},
  editor = {Goodchild, Michael F. and Janelle, Donald G.},
  year = {2004},
  pages = {127--144},
  publisher = {{Oxford University Press}},
  address = {{Oxford}}
}

@article{metzigPreferentialAttachmentSystems,
  title = {Preferential {{Attachment}} in {{Systems}} and {{Networks}} of {{Constant Size}}.},
  author = {Metzig, Cornelia and Colijn, Caroline},
  doi = {arXiv:1811.04972v1},
  abstract = {{$<$}p{$>$}We propose a preferential-attachment-type model for a system of constant size which applies to urn/ball systems and to networks. It yields a power law for the size (or degree) distribution with exponential cutoff depending on parameters. This distribution can be explained by maximization of the Gibbs-Shannon entropy, using as constraint information on the growth of individual urns, or alternatively calculating the exact probabilities. Another distribution that often occurs together with power laws, a `tent-shaped' growth rate distribution, comes out naturally from this model. We confirm our theoretical results with numerical simulations and by another method using recusively calculated exact probabilities. {$<$}/p{$>$}},
  journal = {arXiv Physics and Society}
}

@article{metzigPreferentialAttachmentSystemsa,
  title = {Preferential {{Attachment}} in {{Systems}} and {{Networks}} of {{Constant Size}}.},
  author = {Metzig, Cornelia and Colijn, Caroline},
  doi = {arXiv:1811.04972v1},
  abstract = {{$<$}p{$>$}We propose a preferential-attachment-type model for a system of constant size which applies to urn/ball systems and to networks. It yields a power law for the size (or degree) distribution with exponential cutoff depending on parameters. This distribution can be explained by maximization of the Gibbs-Shannon entropy, using as constraint information on the growth of individual urns, or alternatively calculating the exact probabilities. Another distribution that often occurs together with power laws, a `tent-shaped' growth rate distribution, comes out naturally from this model. We confirm our theoretical results with numerical simulations and by another method using recusively calculated exact probabilities. {$<$}/p{$>$}},
  journal = {arXiv Physics and Society}
}

@article{michalskiEffectiveInfluenceSpreading2020,
  title = {Effective {{Influence Spreading}} in {{Temporal Networks With Sequential Seeding}}},
  author = {Michalski, Rados{\l}aw and Jankowski, Jaros{\l}aw and Br{\'o}dka, Piotr},
  year = {2020},
  volume = {8},
  pages = {151208--151218},
  issn = {2169-3536},
  doi = {10.1109/ACCESS.2020.3016913},
  abstract = {The spread of influence in networks is a topic of great importance in many application areas. For instance, one would like to maximise the coverage, limiting the budget for marketing campaign initialisation and use the potential of social influence. To tackle this and similar challenges, more than a decade ago, researchers started to investigate the influence maximisation problem. The challenge is to find the best set of initially activated seed nodes in order to maximise the influence spread in networks. In typical approach we will activate all seeds in single stage, at the beginning of the process, while in this work we introduce and evaluate a new approach for seeds activation in temporal networks based on sequential seeding. Instead of activating all nodes at the same time, this method distributes the activations of seeds, leading to higher ranges of influence spread. The results of experiments performed using real and randomised networks demonstrate that the proposed method outperforms single stage seeding in 71\% of cases by nearly 6\% on average. Knowing that temporal networks are an adequate choice for modelling dynamic processes, the results of this work can be interpreted as encouraging to apply temporal sequential seeding for real world cases, especially knowing that more sophisticated seed selection strategies can be implemented by using the seed activation strategy introduced in this work.},
  file = {/home/aleksandar/Zotero/storage/BKSDPKVG/Michalski et al_2020_Effective Influence Spreading in Temporal Networks With Sequential Seeding.pdf;/home/aleksandar/Zotero/storage/FEF4EWBX/9169627.html},
  journal = {IEEE Access}
}

@incollection{michalskiEntropyBasedMeasureInfluence2020,
  title = {Entropy-{{Based Measure}} for {{Influence Maximization}} in {{Temporal Networks}}},
  booktitle = {Computational {{Science}} \textendash{} {{ICCS}} 2020},
  author = {Michalski, Rados{\l}aw and Jankowski, Jaros{\l}aw and Pazura, Patryk},
  editor = {Krzhizhanovskaya, Valeria V. and Z{\'a}vodszky, G{\'a}bor and Lees, Michael H. and Dongarra, Jack J. and Sloot, Peter M. A. and Brissos, S{\'e}rgio and Teixeira, Jo{\~a}o},
  year = {2020},
  volume = {12140},
  pages = {277--290},
  publisher = {{Springer International Publishing}},
  address = {{Cham}},
  doi = {10.1007/978-3-030-50423-6_21},
  abstract = {The challenge of influence maximization in social networks is tackled in many settings and scenarios. However, the most explored variant is looking at how to choose a seed set of a given size, that maximizes the number of activated nodes for selected model of social influence. This has been studied mostly in the area of static networks, yet other kinds of networks, such as multilayer or temporal ones, are also in the scope of recent research. In this work we propose and evaluate the measure based on entropy, that investigates how the neighbourhood of nodes varies over time, and based on that and their activity ranks, the nodes as possible candidates for seeds are selected. This measure applied for temporal networks intends to favor nodes that vary their neighbourhood highly and, thanks to that, are good spreaders for certain influence models. The results demonstrate that for the Independent Cascade Model of social influence the introduced entropy-based metric outperforms typical seed selection heuristics for temporal networks. Moreover, compared to some other heuristics, it is fast to compute, thus can be used for fast-varying temporal networks.},
  file = {/home/aleksandar/Dropbox/Zotero/Michalski et al/Michalski et al_2020_Entropy-Based Measure for Influence Maximization in Temporal Networks2.pdf},
  isbn = {978-3-030-50422-9 978-3-030-50423-6},
  keywords = {TODO},
  language = {en}
}

@inproceedings{michalskiEntropyBasedMeasureInfluence2020a,
  title = {Entropy-{{Based Measure}} for {{Influence Maximization}} in {{Temporal Networks}}},
  booktitle = {Computational {{Science}} \textendash{} {{ICCS}} 2020},
  author = {Michalski, Rados{\l}aw and Jankowski, Jaros{\l}aw and Pazura, Patryk},
  editor = {Krzhizhanovskaya, Valeria V. and Z{\'a}vodszky, G{\'a}bor and Lees, Michael H. and Dongarra, Jack J. and Sloot, Peter M. A. and Brissos, S{\'e}rgio and Teixeira, Jo{\~a}o},
  year = {2020},
  pages = {277--290},
  publisher = {{Springer International Publishing}},
  address = {{Cham}},
  doi = {10.1007/978-3-030-50423-6_21},
  abstract = {The challenge of influence maximization in social networks is tackled in many settings and scenarios. However, the most explored variant is looking at how to choose a seed set of a given size, that maximizes the number of activated nodes for selected model of social influence. This has been studied mostly in the area of static networks, yet other kinds of networks, such as multilayer or temporal ones, are also in the scope of recent research. In this work we propose and evaluate the measure based on entropy, that investigates how the neighbourhood of nodes varies over time, and based on that and their activity ranks, the nodes as possible candidates for seeds are selected. This measure applied for temporal networks intends to favor nodes that vary their neighbourhood highly and, thanks to that, are good spreaders for certain influence models. The results demonstrate that for the Independent Cascade Model of social influence the introduced entropy-based metric outperforms typical seed selection heuristics for temporal networks. Moreover, compared to some other heuristics, it is fast to compute, thus can be used for fast-varying temporal networks.},
  file = {/home/aleksandar/Zotero/storage/A993GFX9/Michalski et al_2020_Entropy-Based Measure for Influence Maximization in Temporal Networks.pdf},
  isbn = {978-3-030-50423-6},
  language = {en},
  series = {Lecture {{Notes}} in {{Computer Science}}}
}

@book{migonStatisticalInferenceIntegrated2015,
  title = {Statistical Inference: An Integrated Approach},
  shorttitle = {Statistical Inference},
  author = {Migon, Helio dos Santos and Gamerman, Dani and {Louzada-Neto}, Francisco},
  year = {2015},
  annotation = {OCLC: 951725516},
  isbn = {978-1-4398-7882-8},
  language = {English}
}

@article{mihajlovicDetectingCommunitiesNetworks,
  title = {Detecting Communities in Networks Using {{Belief Propagation}} Algorithm},
  author = {Mihajlovic, Jelena and Bajovic, Dr Dragana},
  pages = {52},
  file = {/home/aleksandar/Zotero/storage/PJVZFMVG/Mihajlovic and Bajovic - Detecting communities in networks using Belief Pro.pdf},
  language = {en}
}

@article{milesMeasuringAutomaticCognition2019,
  title = {Measuring {{Automatic Cognition}}: {{Advancing Dual}}-{{Process Research}} in {{Sociology}}},
  shorttitle = {Measuring {{Automatic Cognition}}},
  author = {Miles, Andrew and {Charron-Ch{\'e}nier}, Rapha{\"e}l and Schleifer, Cyrus},
  year = {2019},
  month = apr,
  volume = {84},
  pages = {308--333},
  issn = {0003-1224, 1939-8271},
  doi = {10.1177/0003122419832497},
  journal = {American Sociological Review},
  language = {en},
  number = {2}
}

@article{milesReGenesisValues2015,
  title = {The ({{Re}})Genesis of {{Values}}: {{Examining}} the {{Importance}} of {{Values}} for {{Action}}},
  shorttitle = {The ({{Re}})Genesis of {{Values}}},
  author = {Miles, Andrew},
  year = {2015},
  month = aug,
  volume = {80},
  pages = {680--704},
  issn = {0003-1224, 1939-8271},
  doi = {10.1177/0003122415591800},
  journal = {American Sociological Review},
  language = {en},
  number = {4}
}

@book{millardElectionsPartiesRepresentation2014,
  title = {Elections, Parties and Representation in Post-Communist Europe},
  author = {Millard, F},
  year = {2014},
  publisher = {{Palgrave Macmillan}},
  address = {{London}},
  annotation = {OCLC: 951523581},
  isbn = {978-1-349-50996-6},
  language = {English}
}

@article{millerAccuracyValueVoter2020,
  title = {The {{Accuracy}} and {{Value}} of {{Voter Validation}} in {{National Surveys}}: {{Insights}} from {{Longitudinal}} and {{Cross}}-{{Sectional Studies}}},
  shorttitle = {The {{Accuracy}} and {{Value}} of {{Voter Validation}} in {{National Surveys}}},
  author = {Miller, Jon D. and Kalmbach, Jason and Woods, Logan T. and Cepuran, Claire},
  year = {2020},
  month = feb,
  pages = {1065912920903432},
  publisher = {{SAGE Publications Inc}},
  issn = {1065-9129},
  doi = {10.1177/1065912920903432},
  abstract = {Ansolabehere and Hersh and others have examined the reported voting behavior of survey respondents using a variety of validation methods, including matching with national voter files provided by outside vendors. This analysis provides the first examination of a thirty-year national longitudinal study and compares the insights obtained from this longitudinal analysis to two 2016 national cross-sectional studies of voting behavior using structural equation modeling. We find that respondents of the longitudinal study overreport at lower rates than respondents in our 2016 samples, and the traditional predictors of overreporting such as political interest, engagement, and partisanship predict overreporting among respondents in both our longitudinal and 2016 short-term panel studies, but our longitudinal data include novel predictors of overreporting such as parent socialization factors. We conclude with a discussion of the phenomenon of overreporting in surveys and how survey accuracy becomes increasingly important for both the public and policymakers in an era of decreasing trust in institutions and expertise.},
  journal = {Political Research Quarterly},
  language = {en}
}

@book{millsapStatisticalApproachesMeasurement2011,
  title = {Statistical Approaches to Measurement Invariance},
  author = {Millsap, Roger Ellis},
  year = {2011},
  publisher = {{Routledge}},
  address = {{New York London}},
  annotation = {OCLC: 846122484},
  file = {/home/aleksandar/Dropbox/Zotero/Millsap/Millsap_2011_Statistical approaches to measurement invariance.pdf},
  isbn = {978-1-84872-818-9 978-1-84872-819-6},
  language = {en}
}

@article{minhasInferentialApproachesNetwork2019,
  ids = {minhasInferentialApproachesNetwork,minhasInferentialApproachesNetworka,minhasInferentialApproachesNetworkb},
  title = {Inferential {{Approaches}} for {{Network Analysis}}: {{AMEN}} for {{Latent Factor Models}}},
  shorttitle = {Inferential {{Approaches}} for {{Network Analysis}}},
  author = {Minhas, Shahryar and Hoff, Peter D. and Ward, Michael D.},
  year = {2019},
  month = apr,
  volume = {27},
  pages = {208--222},
  issn = {1047-1987, 1476-4989},
  doi = {10.1017/pan.2018.50},
  abstract = {We introduce a Bayesian approach to conduct inferential analyses on dyadic data while accounting for interdependencies between observations through a set of additive and multiplicative effects (AME). The AME model is built on a generalized linear modeling framework and is thus flexible enough to be applied to a variety of contexts. We contrast the AME model to two prominent approaches in the literature: the latent space model (LSM) and the exponential random graph model (ERGM). Relative to these approaches, we show that the AME approach is (a) to be easy to implement; (b) interpretable in a general linear model framework; (c) computationally straightforward; (d) not prone to degeneracy; (e) captures first-, second-, and third-order network dependencies; and (f) notably outperforms ERGMs and LSMs on a variety of metrics and in an out-of-sample context. In summary, AME offers a straightforward way to undertake nuanced, principled inferential network analysis for a wide range of social science questions.},
  file = {/home/aleksandar/Zotero/storage/9Y7N6MAZ/C5766B1FC01B5C875500B5724F162889.html},
  journal = {Political Analysis},
  language = {en},
  number = {2}
}

@article{miocevicDifferentRolesPrior,
  title = {Different {{Roles}} of {{Prior Distributions}} in the {{Single Mediator Model}} with {{Latent Variables}}},
  author = {Mio{\v c}evi{\'c}, Milica and Levy, Roy and MacKinnon, David P.},
  doi = {10.1080/00273171.2019.1709405},
  abstract = {In manifest variable models, Bayesian methods for mediation analysis can have better statistical properties than commonly used frequentist methods. However, with latent variables, Bayesian mediation analysis with diffuse priors can yield worse statistical properties than frequentist methods, and no study to date has evaluated the impact of informative priors on statistical properties of point and interval summaries of the mediated effect. This article describes the first examination of using fully conjugate and informative (accurate and inaccurate) priors in Bayesian mediation analysis with latent variables. Results suggest that fully conjugate priors and informative priors with the same relative prior sample sizes have notably different effects at \emph{N}\,=\,200 and 400, than at \emph{N}\,=\,50 and 100. Consequences of a small amount of inaccuracy in priors for loadings can be alleviated by making the prior less informative, whereas the same is not always true of inaccuracy in priors for structural paths. Finally, the consequences of using informative priors depend on the inferential goals of the analysis: inaccurate priors are more detrimental for accurately estimating the mediated effect than for evaluating whether the mediated effect is nonzero. Recommendations are provided about when to gainfully employ Bayesian mediation analysis with latent variables.},
  journal = {Multivariate Behavioral Research}
}

@article{miocevicDifferentRolesPriora,
  title = {Different {{Roles}} of {{Prior Distributions}} in the {{Single Mediator Model}} with {{Latent Variables}}},
  author = {Mio{\v c}evi{\'c}, Milica and Levy, Roy and MacKinnon, David P.},
  doi = {10.1080/00273171.2019.1709405},
  abstract = {In manifest variable models, Bayesian methods for mediation analysis can have better statistical properties than commonly used frequentist methods. However, with latent variables, Bayesian mediation analysis with diffuse priors can yield worse statistical properties than frequentist methods, and no study to date has evaluated the impact of informative priors on statistical properties of point and interval summaries of the mediated effect. This article describes the first examination of using fully conjugate and informative (accurate and inaccurate) priors in Bayesian mediation analysis with latent variables. Results suggest that fully conjugate priors and informative priors with the same relative prior sample sizes have notably different effects at \emph{N}\,=\,200 and 400, than at \emph{N}\,=\,50 and 100. Consequences of a small amount of inaccuracy in priors for loadings can be alleviated by making the prior less informative, whereas the same is not always true of inaccuracy in priors for structural paths. Finally, the consequences of using informative priors depend on the inferential goals of the analysis: inaccurate priors are more detrimental for accurately estimating the mediated effect than for evaluating whether the mediated effect is nonzero. Recommendations are provided about when to gainfully employ Bayesian mediation analysis with latent variables.},
  journal = {Multivariate Behavioral Research}
}

@article{miocevicDifferentRolesPriorb,
  title = {Different {{Roles}} of {{Prior Distributions}} in the {{Single Mediator Model}} with {{Latent Variables}}},
  author = {Mio{\v c}evi{\'c}, Milica and Levy, Roy and MacKinnon, David P.},
  doi = {10.1080/00273171.2019.1709405},
  abstract = {In manifest variable models, Bayesian methods for mediation analysis can have better statistical properties than commonly used frequentist methods. However, with latent variables, Bayesian mediation analysis with diffuse priors can yield worse statistical properties than frequentist methods, and no study to date has evaluated the impact of informative priors on statistical properties of point and interval summaries of the mediated effect. This article describes the first examination of using fully conjugate and informative (accurate and inaccurate) priors in Bayesian mediation analysis with latent variables. Results suggest that fully conjugate priors and informative priors with the same relative prior sample sizes have notably different effects at \emph{N}\,=\,200 and 400, than at \emph{N}\,=\,50 and 100. Consequences of a small amount of inaccuracy in priors for loadings can be alleviated by making the prior less informative, whereas the same is not always true of inaccuracy in priors for structural paths. Finally, the consequences of using informative priors depend on the inferential goals of the analysis: inaccurate priors are more detrimental for accurately estimating the mediated effect than for evaluating whether the mediated effect is nonzero. Recommendations are provided about when to gainfully employ Bayesian mediation analysis with latent variables.},
  journal = {Multivariate Behavioral Research}
}

@article{miokBANNotBAN2020,
  title = {To {{BAN}} or Not to {{BAN}}: {{Bayesian Attention Networks}} for {{Reliable Hate Speech Detection}}},
  shorttitle = {To {{BAN}} or Not to {{BAN}}},
  author = {Miok, Kristian and Skrlj, Blaz and Zaharie, Daniela and {Robnik-Sikonja}, Marko},
  year = {2020},
  month = jul,
  abstract = {Hate speech is an important problem in the management of user generated content. In order to remove offensive content or ban misbehaving users, content moderators need reliable hate speech detectors. Recently, deep neural networks based on transformer architecture, such as (multilingual) BERT model, achieve superior performance in many natural language classification tasks, including hate speech detection. So far, these methods have not been able to quantify their output in terms of reliability. We propose a Bayesian method using Monte Carlo Dropout within the attention layers of the transformer models to provide well-calibrated reliability estimates. We evaluate and visualize the introduced approach on hate speech detection problems in several languages. From the experiments performed it was observed that our approach significantly improve the hate speech detection that can not be trusted. Our approach not only improves classification performance of the state-of-the-art multilingual BERT model, but the computed reliability scores also significantly reduce the workload in inspection of offending cases and in reannotation campaigns. The provided visualization helps to understand the borderline outcomes.},
  archivePrefix = {arXiv},
  eprint = {2007.05304},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Zotero/storage/SB7SJU6P/Miok et al. - 2020 - To BAN or not to BAN Bayesian Attention Networks .pdf},
  journal = {arXiv:2007.05304 [stat]},
  language = {en},
  primaryClass = {stat}
}

@article{mirandafilhoSurvivingZombieApocalypse00,
  title = {Surviving the {{Zombie Apocalypse}}: {{A Population Dynamics Based Approach}}},
  shorttitle = {Surviving the {{Zombie Apocalypse}}},
  author = {Miranda Filho, L. H. and Ribeiro, Bruno V. and Rocha, Paulo M. M. and Santos, Daniel D. A. and de Sena, Nat{\'a}lia C. and Miranda Filho, L. H. and Ribeiro, Bruno V. and Rocha, Paulo M. M. and Santos, Daniel D. A. and de Sena, Nat{\'a}lia C.},
  year = {2020},
  volume = {42},
  publisher = {{Sociedade Brasileira de F\'isica}},
  issn = {1806-1117},
  doi = {10.1590/1806-9126-rbef-2020-0071},
  file = {/home/aleksandar/Dropbox/Zotero/Miranda Filho et al/Miranda Filho et al_2020_Surviving the Zombie Apocalypse.pdf;/home/aleksandar/Zotero/storage/MEVMYNYF/scielo.html},
  journal = {Revista Brasileira de Ensino de F\'isica},
  language = {en}
}

@article{mirmousaviLinkPredictionUsing,
  title = {Link {{Prediction Using Network Embedding Based}} on {{Global Similarity}}},
  author = {Mirmousavi, S F and Kianian, S},
  pages = {12},
  abstract = {Background: The link prediction issue is one of the most widely used problems in complex network analysis. Link prediction requires knowing the background of previous link connections and combining them with available information. The link prediction local approaches with node structure objectives are fast in case of speed but are not accurate enough. On the other hand, the global link prediction methods identify all path structures in a network and can determine the similarity degree between graph-extracted entities with high accuracy but are time-consuming instead. Most existing algorithms are only using one type of feature (global or local) to represent data, which not well described due to the large scale and heterogeneity of complex networks.},
  file = {/home/aleksandar/Zotero/storage/3ZWT26A5/Mirmousavi and Kianian - Link Prediction Using Network Embedding Based on G.pdf},
  language = {en}
}

@article{miuCulturalAdaptationMaximisedundefined/ed,
  title = {Cultural Adaptation Is Maximised When Intelligent Individuals Rarely Think for Themselves},
  author = {Miu, Elena and Morgan, Thomas J. H.},
  year = {undefined/ed},
  pages = {1--34},
  publisher = {{Cambridge University Press}},
  issn = {2513-843X},
  doi = {10.1017/ehs.2020.42},
  abstract = {//static.cambridge.org/content/id/urn\%3Acambridge.org\%3Aid\%3Aarticle\%3AS2513843X20000420/resource/name/firstPage-S2513843X20000420a.jpg},
  file = {/home/aleksandar/Documents/Zotero/Miu_Morgan/Miu_Morgan_undefineded_Cultural adaptation is maximised when intelligent individuals rarely think for.pdf;/home/aleksandar/Zotero/storage/QMUTHU6X/9C06326BEAB863A1F165C5E592F839BB.html},
  journal = {Evolutionary Human Sciences},
  language = {en}
}

@inproceedings{miyagawaHowLevyFlights2020,
  title = {How {{L\'evy Flights Triggered}} by {{Presence}} of {{Defectors Affect Evolution}} of {{Cooperation}} in {{Spatial Games}}},
  booktitle = {The 2020 {{Conference}} on {{Artificial Life}}},
  author = {Miyagawa, Daiki and Ichinose, Genki and Chiba, Erika and Sayama, Hiroki},
  year = {2020},
  pages = {715--718},
  publisher = {{MIT Press}},
  address = {{Online}},
  doi = {10.1162/isal_a_00272},
  abstract = {Cooperation among individuals has been key to sustaining societies. However, natural selection favors defection over cooperation. Cooperation can be favored when the mobility of individuals allows cooperators to form a cluster (or group). Mobility patterns of animals sometimes follow a Le\textasciiacute vy flight. A Le\textasciiacute vy flight is a kind of random walk but it is composed of many small movements with a few big movements. Here, we developed an agent-based model in a square lattice where agents perform Le\textasciiacute vy flights depending on the fraction of neighboring defectors. We focus on how the sensitivity to defectors when performing Le\textasciiacute vy flights promotes the evolution of cooperation. Results of evolutionary simulations showed that cooperation was most promoted when the sensitivity to defectors was moderate. As the population density became larger, higher sensitivity was more beneficial for cooperation to evolve.},
  file = {/home/aleksandar/Zotero/storage/S24HA9VM/Miyagawa et al. - 2020 - How Lévy Flights Triggered by Presence of Defector.pdf},
  language = {en}
}

@article{mizutakaEmergenceLongRangeCorrelations2020,
  title = {Emergence of {{Long}}-{{Range Correlations}} in {{Random Networks}}},
  author = {Mizutaka, Shogo and Hasegawa, Takehisa},
  year = {2020},
  month = jun,
  abstract = {We perform an analytical analysis of the long-range degree correlation of the giant component in an uncorrelated random network by employing generating functions. By introducing a characteristic length, we find that a pair of nodes in the giant component is negatively degree-correlated within the characteristic length and uncorrelated otherwise. At the critical point, where the giant component becomes fractal, the characteristic length diverges and the negative long-range degree correlation emerges. We further propose a correlation function for degrees of the \$l\$-distant node pairs, which behaves as an exponentially decreasing function of distance in the off-critical region. The correlation function obeys a power-law with an exponential cutoff near the critical point. The Erd\textbackslash H\{o\}s-R\textbackslash '\{e\}nyi random graph is employed to confirm this critical behavior.},
  archivePrefix = {arXiv},
  eprint = {2006.08132},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Dropbox/Zotero/Mizutaka_Hasegawa/Mizutaka_Hasegawa_2020_Emergence of Long-Range Correlations in Random Networks.pdf;/home/aleksandar/Zotero/storage/K9I9LGUG/2006.html},
  journal = {arXiv:2006.08132 [cond-mat, physics:physics]},
  primaryClass = {cond-mat, physics:physics}
}

@article{mizutakaEmergenceLongrangeCorrelations2020,
  title = {Emergence of Long-Range Correlations in Random Networks},
  author = {Mizutaka, Shogo and Hasegawa, Takehisa},
  year = {2020},
  month = sep,
  volume = {1},
  pages = {035007},
  publisher = {{IOP Publishing}},
  issn = {2632-072X},
  doi = {10.1088/2632-072X/abb4c5},
  abstract = {We perform an analysis of the long-range degree correlation of the giant component (GC) in an uncorrelated random network by employing generating functions. By introducing a characteristic length, we find that a pair of nodes in the GC is negatively degree-correlated within the characteristic length and uncorrelated otherwise. At the critical point, where the GC becomes fractal, the characteristic length diverges and the negative long-range degree correlation emerges. We further propose a correlation function for degrees of two nodes separated by the shortest path length l, which behaves as an exponentially decreasing function of distance in the off-critical region. The correlation function obeys a power-law with an exponential cutoff near the critical point. The Erd\H{o}s-R\'enyi random graph is employed to confirm this critical behavior.},
  file = {/home/aleksandar/Dropbox/Zotero/Mizutaka_Hasegawa/Mizutaka_Hasegawa_2020_Emergence of long-range correlations in random networks.pdf},
  journal = {Journal of Physics: Complexity},
  language = {en},
  number = {3}
}

@article{mohantyMessyDataRobust,
  ids = {mohantyMessyDataRobusta},
  title = {Messy {{Data}}, {{Robust Inference}}? {{Navigating Obstacles}} to {{Inference}} with {{bigKRLS}}},
  author = {Mohanty, Pete and Shaffer, Robert},
  doi = {10.1017/pan.2018.33},
  abstract = {Complex models are of increasing interest to social scientists. Researchers interested in prediction generally favor flexible, robust approaches, while those interested in causation are often interested in modeling nuanced treatment structures and confounding relationships. Unfortunately, estimators of complex models often scale poorly, especially if they seek to maintain interpretability. In this paper, we present an example of such a conundrum and show how optimization can alleviate the worst of these concerns. Specifically, we introduce {$<$}a href="https://github.com/rdrr1990/bigKRLS"{$>$}bigKRLS{$<$}/a{$>$}, which offers a variety of statistical and computational improvements to the Hainmueller and Hazlett ({$<$}a href="\#r14"{$>$}2013{$<$}/a{$>$}) Kernel-Regularized Least Squares (KRLS) approach. As part of our improvements, we decrease the estimator's single-core runtime by 50\% and reduce the estimator's peak memory usage by an order of magnitude. We also improve uncertainty estimates for the model's average marginal effect estimates\textemdash which we test both in simulation and in practice\textemdash and introduce new visual and statistical tools designed to assist with inference under the model. We further demonstrate the value of our improvements through an analysis of the 2016 presidential election, an analysis that would have been impractical or even infeasible for many users with existing software.},
  journal = {Political Analysis}
}

@article{molenaarNewPersonSpecificParadigm2009,
  title = {The {{New Person}}-{{Specific Paradigm}} in {{Psychology}}},
  author = {Molenaar, Peter C.M. and Campbell, Cynthia G.},
  year = {2009},
  month = apr,
  volume = {18},
  pages = {112--117},
  publisher = {{SAGE Publications Inc}},
  issn = {0963-7214},
  doi = {10.1111/j.1467-8721.2009.01619.x},
  abstract = {Most research methodology in the behavioral sciences employs interindividual analyses, which provide information about the state of affairs of the population. However, as shown by classical mathematical-statistical theorems (the ergodic theorems), such analyses do not provide information for, and cannot be applied at, the level of the individual, except on rare occasions when the processes of interest meet certain stringent conditions. When psychological processes violate these conditions, the interindividual analyses that are now standardly applied have to be replaced by analysis of intraindividual variation in order to obtain valid results. Two illustrations involving analysis of intraindividual variation of personality and emotional processes are given.},
  file = {/home/aleksandar/Dropbox/Zotero/Molenaar_Campbell/Molenaar_Campbell_2009_The New Person-Specific Paradigm in Psychology.pdf},
  journal = {Current Directions in Psychological Science},
  keywords = {Big Five Personality Factors,emotional experiences,ergodic conditions,interindividual variation,intraindividual variation,P-technique,R-technique},
  language = {en},
  number = {2}
}

@incollection{moleNationalismHomophobiaCentral2016,
  title = {Nationalism and {{Homophobia}} in {{Central}} and {{Eastern Europe}}},
  booktitle = {The {{EU Enlargement}} and {{Gay Politics}}},
  author = {Mole, Richard C. M.},
  editor = {Slootmaeckers, Koen and Touquet, Heleen and Vermeersch, Peter},
  year = {2016},
  pages = {99--121},
  publisher = {{Palgrave Macmillan UK}},
  address = {{London}},
  doi = {10.1057/978-1-137-48093-4_5},
  isbn = {978-1-137-48092-7 978-1-137-48093-4},
  language = {en}
}

@article{mollasETHOSOnlineHate2020,
  title = {{{ETHOS}}: An {{Online Hate Speech Detection Dataset}}},
  shorttitle = {{{ETHOS}}},
  author = {Mollas, Ioannis and Chrysopoulou, Zoe and Karlos, Stamatis and Tsoumakas, Grigorios},
  year = {2020},
  month = jun,
  abstract = {Online hate speech is a newborn problem in our modern society which is growing at a steady rate exploiting weaknesses of the corresponding regimes that characterise several social media platforms. Therefore, this phenomenon is mainly cultivated through such comments, either during users' interaction or on posted multimedia context. Nowadays, giant companies own platforms where many millions of users log in daily. Thus, protection of their users from exposure to similar phenomena for keeping up with the corresponding law, as well as for retaining a high quality of offered services, seems mandatory. Having a robust and reliable mechanism for identifying and preventing the uploading of related material would have a huge effect on our society regarding several aspects of our daily life. On the other hand, its absence would deteriorate heavily the total user experience, while its erroneous operation might raise several ethical issues. In this work, we present a protocol for creating a more suitable dataset, regarding its both informativeness and representativeness aspects, favouring the safer capture of hate speech occurrence, without at the same time restricting its applicability to other classification problems. Moreover, we produce and publish a textual dataset with two variants: binary and multi-label, called `ETHOS', based on YouTube and Reddit comments validated through figure-eight crowdsourcing platform. Our assumption about the production of more compatible datasets is further investigated by applying various classification models and recording their behaviour over several appropriate metrics.},
  archivePrefix = {arXiv},
  eprint = {2006.08328},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Dropbox/Zotero/Mollas et al/Mollas et al_2020_ETHOS.pdf;/home/aleksandar/Zotero/storage/UQCFKKV3/2006.html},
  journal = {arXiv:2006.08328 [cs, stat]},
  primaryClass = {cs, stat}
}

@article{monroeGeneralConnectionistModel2008,
  title = {A General Connectionist Model of Attitude Structure and Change: {{The ACS}} ({{Attitudes}} as {{Constraint Satisfaction}}) Model.},
  shorttitle = {A General Connectionist Model of Attitude Structure and Change},
  author = {Monroe, Brian M. and Read, Stephen J.},
  year = {2008},
  volume = {115},
  pages = {733--759},
  issn = {1939-1471, 0033-295X},
  doi = {10.1037/0033-295X.115.3.733},
  abstract = {A localist, parallel constraint satisfaction, artificial neural network model is presented that accounts for a broad collection of attitude and attitude-change phenomena. The network represents the attitude object and cognitions and beliefs related to the attitude, as well as how to integrate a persuasive message into this network. Short-term effects are modeled by activation patterns due to parallel constraint satisfaction processes, and long-term effects are modeled by weight changes due to the settling patterns of activation. Phenomena modeled include thought-induced attitude polarization, elaboration and attitude strength, motivated reasoning and social influence, an integrated view of heuristic versus systematic persuasion, and implicit versus explicit attitude change. Results of the simulations are consistent with empirical results. The same set of simple mechanisms is used to model all the phenomena, which allows the model to offer a parsimonious theoretical account of how structure can impact attitude change. This model is compared with previous computational approaches to attitudes, and implications for attitude research are discussed.},
  journal = {Psychological Review},
  language = {en},
  number = {3}
}

@article{montoyaPoorFitModel2020,
  title = {The {{Poor Fit}} of {{Model Fit}} for {{Selecting Number}} of {{Factors}} in {{Exploratory Factor Analysis}} for {{Scale Evaluation}}},
  author = {Montoya, Amanda K. and Edwards, Michael C.},
  year = {2020},
  month = aug,
  pages = {001316442094289},
  issn = {0013-1644, 1552-3888},
  doi = {10.1177/0013164420942899},
  abstract = {Model fit indices are being increasingly recommended and used to select the number of factors in an exploratory factor analysis. Growing evidence suggests that the recommended cutoff values for common model fit indices are not appropriate for use in an exploratory factor analysis context. A particularly prominent problem in scale evaluation is the ubiquity of correlated residuals and imperfect model specification. Our research focuses on a scale evaluation context and the performance of four standard model fit indices: root mean square error of approximate (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI), and Tucker\textendash Lewis index (TLI), and two equivalence test-based model fit indices: RMSEAt and CFIt. We use Monte Carlo simulation to generate and analyze data based on a substantive example using the positive and negative affective schedule ( N = 1,000). We systematically vary the number and magnitude of correlated residuals as well as nonspecific misspecification, to evaluate the impact on model fit indices in fitting a two-factor exploratory factor analysis. Our results show that all fit indices, except SRMR, are overly sensitive to correlated residuals and nonspecific error, resulting in solutions that are overfactored. SRMR performed well, consistently selecting the correct number of factors; however, previous research suggests it does not perform well with categorical data. In general, we do not recommend using model fit indices to select number of factors in a scale evaluation framework.},
  journal = {Educational and Psychological Measurement},
  language = {en}
}

@article{mooreInclusivityEnhancesRobustness2021,
  title = {Inclusivity Enhances Robustness and Efficiency of Social Networks},
  author = {Moore, Jack Murdoch and Small, Michael and Yan, Gang},
  year = {2021},
  month = feb,
  volume = {563},
  pages = {125490},
  issn = {0378-4371},
  doi = {10.1016/j.physa.2020.125490},
  abstract = {Many modern institutions seek to be inclusive, but the quantitative benefits of this goal are not always communicated effectively to stakeholders. To facilitate this important dialogue, we propose a simple model with which to grow, in the presence of a given level of inclusivity, networks which represent the structure of organisations. The model proceeds via an unweighted random walk in which inclusivity r is the maximum allowable separation between a new contact and the set of established contacts, which thus represents the maximum tolerable amount of novelty or ``otherness''. The model can capture realistic small world and scale-free properties. In addition, the model fixes the limiting degree distribution, and particular parameter choices and initial conditions also fix clustering coefficient, and so allows the role of inclusivity to be isolated from these confounding factors. By considering this model, and also by randomly rewiring real networks in either an inclusive or an exclusive way, we show that, in comparison to exclusivity, inclusivity promotes unity (by decreasing modularity), efficiency and robustness. Increasing the ratio of the number of links to the number of nodes also enhances these qualities, as well as reducing their dependence upon inclusivity.},
  journal = {Physica A: Statistical Mechanics and its Applications},
  keywords = {Efficiency,Inclusivity,Network models,Robustness,Scale-free networks,Small world networks},
  language = {en}
}

@article{mooreInclusivityEnhancesRobustness2021a,
  title = {Inclusivity Enhances Robustness and Efficiency of Social Networks},
  author = {Moore, Jack Murdoch and Small, Michael and Yan, Gang},
  year = {2021},
  month = feb,
  volume = {563},
  pages = {125490},
  issn = {0378-4371},
  doi = {10.1016/j.physa.2020.125490},
  abstract = {Many modern institutions seek to be inclusive, but the quantitative benefits of this goal are not always communicated effectively to stakeholders. To facilitate this important dialogue, we propose a simple model with which to grow, in the presence of a given level of inclusivity, networks which represent the structure of organisations. The model proceeds via an unweighted random walk in which inclusivity r is the maximum allowable separation between a new contact and the set of established contacts, which thus represents the maximum tolerable amount of novelty or ``otherness''. The model can capture realistic small world and scale-free properties. In addition, the model fixes the limiting degree distribution, and particular parameter choices and initial conditions also fix clustering coefficient, and so allows the role of inclusivity to be isolated from these confounding factors. By considering this model, and also by randomly rewiring real networks in either an inclusive or an exclusive way, we show that, in comparison to exclusivity, inclusivity promotes unity (by decreasing modularity), efficiency and robustness. Increasing the ratio of the number of links to the number of nodes also enhances these qualities, as well as reducing their dependence upon inclusivity.},
  file = {/home/aleksandar/Zotero/storage/K2SKJ9HW/S0378437120307895.html},
  journal = {Physica A: Statistical Mechanics and its Applications},
  keywords = {Efficiency,Inclusivity,Network models,Robustness,Scale-free networks,Small world networks},
  language = {en}
}

@article{moradiEvolutionaryMethodCommunity,
  title = {An Evolutionary Method for Community Detection Using a Novel Local Search Strategy},
  author = {Moradi, Mehdi and Parsa, Saeed},
  doi = {10.1016/j.physa.2019.01.133},
  abstract = {{$<$}div{$><$}h2{$>$}Abstract{$<$}/h2{$><$}p{$>$}Community detection is an NP-hard problem. Therefore, evolutionary-based optimization methods are conventionally applied to cope with the problem. The primary challenge regarding the application of evolutionary-based approaches, specifically to handle large complex networks, is their relatively long execution time. In this respect, this article proposes an extension of a known genetic algorithm, Genetic Algorithm for Community Detection (GACD), for community detection. This new extension is supplied with a novel local search strategy to speed up the convergence and improve the accuracy of the GACD algorithm. To reduce the search space, a locus-based representation of the complex network, in which communities are to be detected, is applied. This type of representation incorporates domain-specific knowledge with the solutions through initialization and reproduction operators. In addition, it does not need to know the number of communities at the beginning of the search process. Our experiments with the real-world and Lancichinetti-Fortunato-Radicchi (LFR) network datasets demonstrate the relatively high capacity of our proposed genetic algorithm in detecting communities with relatively fewer generations and more precision.{$<$}/p{$><$}/div{$>$}},
  journal = {Physica A: Statistical Mechanics and its Applications}
}

@article{moradiEvolutionaryMethodCommunitya,
  title = {An Evolutionary Method for Community Detection Using a Novel Local Search Strategy},
  author = {Moradi, Mehdi and Parsa, Saeed},
  doi = {10.1016/j.physa.2019.01.133},
  abstract = {{$<$}div{$><$}h2{$>$}Abstract{$<$}/h2{$><$}p{$>$}Community detection is an NP-hard problem. Therefore, evolutionary-based optimization methods are conventionally applied to cope with the problem. The primary challenge regarding the application of evolutionary-based approaches, specifically to handle large complex networks, is their relatively long execution time. In this respect, this article proposes an extension of a known genetic algorithm, Genetic Algorithm for Community Detection (GACD), for community detection. This new extension is supplied with a novel local search strategy to speed up the convergence and improve the accuracy of the GACD algorithm. To reduce the search space, a locus-based representation of the complex network, in which communities are to be detected, is applied. This type of representation incorporates domain-specific knowledge with the solutions through initialization and reproduction operators. In addition, it does not need to know the number of communities at the beginning of the search process. Our experiments with the real-world and Lancichinetti-Fortunato-Radicchi (LFR) network datasets demonstrate the relatively high capacity of our proposed genetic algorithm in detecting communities with relatively fewer generations and more precision.{$<$}/p{$><$}/div{$>$}},
  journal = {Physica A: Statistical Mechanics and its Applications}
}

@article{moranInterpretationStatisticalMaps1948,
  title = {The {{Interpretation}} of Statistical Maps},
  author = {Moran, P. A. P.},
  year = {1948},
  volume = {10},
  pages = {243--251},
  issn = {00359246},
  journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
  number = {2}
}

@article{morel-balbiNullModelsMultioptimized2020,
  title = {Null Models for Multi-Optimized Large-Scale Network Structures},
  author = {{Morel-Balbi}, Sebastian and Peixoto, Tiago P.},
  year = {2020},
  month = jun,
  abstract = {We study the emerging large-scale structures in networks subject to selective pressures that simultaneously drive towards higher modularity and robustness against random failures. We construct maximum-entropy null models that isolate the effects of the joint optimization on the network structure from any kind of evolutionary dynamics. Our analysis reveals a rich phase diagram of optimized structures, composed of many combinations of modular, core-periphery and bipartite patterns. Furthermore, we observe parameter regions where the simultaneous optimization can be either synergistic or antagonistic, with the improvement of one criterion directly aiding or hindering the other, respectively. Our results show how interactions between different selective pressures can be pivotal in determining the emerging network structure, and that these interactions can be captured by simple network models.},
  archivePrefix = {arXiv},
  eprint = {2006.08508},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Dropbox/Zotero/Morel-Balbi_Peixoto/Morel-Balbi_Peixoto_2020_Null models for multi-optimized large-scale network structures.pdf;/home/aleksandar/Zotero/storage/M5976PM6/2006.html},
  journal = {arXiv:2006.08508 [cond-mat, physics:nlin, physics:physics]},
  primaryClass = {cond-mat, physics:nlin, physics:physics}
}

@article{morenoZealotryInfluenceMaximization2020,
  title = {Zealotry and {{Influence Maximization}} in the {{Voter Model}}: {{When}} to {{Target Zealots}}?},
  shorttitle = {Zealotry and {{Influence Maximization}} in the {{Voter Model}}},
  author = {Moreno, Guillermo Romero and Manino, Edoardo and {Tran-Thanh}, Long and Brede, Markus},
  year = {2020},
  pages = {107--118},
  doi = {10.1007/978-3-030-40943-2_10},
  abstract = {In this paper, we study influence maximization in the voter model in the presence of biased voters (or zealots) on complex networks. Under what conditions should an external controller with finite budget who aims at maximizing its influence over the system target zealots? Our analysis, based on both analytical and numerical results, shows a rich diagram of preferences and degree-dependencies of allocations to zealots and normal agents varying with the budget. We find that when we have a large budget or for low levels of zealotry, optimal strategies should give larger allocations to zealots and allocations are positively correlated with node degree. In contrast, for low budgets or highly-biased zealots, optimal strategies give higher allocations to normal agents, with some residual allocations to zealots, and allocations to both types of agents decrease with node degree. Our results emphasize that heterogeneity in agent properties strongly affects strategies for influence maximization on heterogeneous networks.},
  archivePrefix = {arXiv},
  eprint = {2008.10012},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Moreno et al/Moreno et al_2020_Zealotry and Influence Maximization in the Voter Model.pdf;/home/aleksandar/Zotero/storage/G37Z8MHE/2008.html},
  journal = {arXiv:2008.10012 [physics]},
  primaryClass = {physics}
}

@article{morrisonIrishUniversityStudents2009,
  title = {Irish {{University Students}}' {{Support}} for the {{Human Rights}} of {{Lesbian Women}} and {{Gay Men}}},
  author = {Morrison, Todd G. and Speakman, Caoimhe and Ryan, Travis A.},
  year = {2009},
  month = apr,
  volume = {56},
  pages = {387--400},
  issn = {0091-8369, 1540-3602},
  doi = {10.1080/00918360902728871},
  journal = {Journal of Homosexuality},
  language = {en},
  number = {3}
}

@article{morselliEconomicConditionsSocial,
  title = {Economic Conditions and Social Trust Climates in {{Europe}} over Ten Years: {{An}} Ecological Analysis of Change},
  author = {Morselli, Davide and Glaeser, Stephanie},
  doi = {10.1080/21515581.2018.1442722},
  abstract = {{$<$}p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:oasis="http://docs.oasis-open.org/ns/oasis-exchange/table"{$>$}Two concurrent positions have driven research on the relationship between economic factors and social trust across countries: While some research has shown that unequal wealth distribution leads to poor social trust, other research has argued that social trust is the precondition to a country's economic performance and distribution of economic resources. Using an ecological linear growth model, this study tests these two concurrent positions with data from the first six rounds of the European Social Survey (ESS). This study focuses on the links between socio-economic conditions and inclusive social capital climates, i.e. social climates where inclusive attitudes and generalised trust are widely extended to outgroups. Two models are estimated with Bayesian methods and then compared. The results support the hypothesis that the diffusion of inclusive social capital climates can predict the improvement of a country's socio-economic conditions. However, they also support the opposite hypothesis, according to which the improvement of socio-economic conditions is pivotal in creating a climate of trust. Slightly stronger results are found for the latter hypothesis, suggesting that the enhancement of economic conditions and income distribution may be pivotal in reinforcing the social fabric.{$<$}/p{$>$}},
  journal = {Journal of Trust Research}
}

@incollection{mortonFormalModelingEmpirical2009,
  title = {Formal {{Modeling}} and {{Empirical Analysis}} in {{Political Science}}},
  booktitle = {Methoden Der Vergleichenden {{Politik}}- Und {{Sozialwissenschaft}}},
  author = {Morton, Rebecca B.},
  editor = {Pickel, Susanne and Pickel, Gert and Lauth, Hans-Joachim and Jahn, Detlef},
  year = {2009},
  pages = {27--35},
  publisher = {{VS Verlag f\"ur Sozialwissenschaften}},
  address = {{Wiesbaden}},
  doi = {10.1007/978-3-531-91826-6_2},
  file = {/home/aleksandar/Dropbox/Zotero/Morton/Morton_2009_Formal Modeling and Empirical Analysis in Political Science2.pdf},
  isbn = {978-3-531-16194-5 978-3-531-91826-6},
  language = {en}
}

@techreport{moslehSharedPartisanshipDramatically2020,
  title = {Shared {{Partisanship Dramatically Increases Social Tie Formation}} in a {{Twitter Field Experiment}}},
  author = {Mosleh, Mohsen and Martel, Cameron and Eckles, Dean and Rand, David Gertler},
  year = {2020},
  month = oct,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/ykh5t},
  abstract = {Americans are much more likely to be socially connected to co-partisans, both in daily life and on social media. But this observation does not necessarily mean that shared partisanship per se drives social tie formation, because partisanship is confounded with many other factors. Here, we test the causal effect of shared partisanship on the formation of social ties in a field experiment on Twitter. We created bot accounts that self-identified as people who favored the Democratic or Republican party, and that varied in the strength of that identification. We then randomly assigned 842 Twitter users to be followed by one of our accounts. Users were roughly three times more likely to reciprocally follow-back bots whose partisanship matched their own, and this was true regardless of the bot's strength of identification. Interestingly, there was no partisan asymmetry in this preferential follow-back behavior: Democrats and Republicans alike were much more likely to reciprocate follows from co-partisans. These results demonstrate a strong causal effect of shared partisanship on the formation of social ties in an ecologically valid field setting, and have important implications for political psychology, social media, and the politically polarized state of the American public.},
  file = {/home/aleksandar/Zotero/storage/F5J8I8W2/Mosleh et al. - 2020 - Shared Partisanship Dramatically Increases Social .pdf},
  language = {en},
  type = {Preprint}
}

@techreport{moslehSharedPartisanshipDramatically2020a,
  title = {Shared {{Partisanship Dramatically Increases Social Tie Formation}} in a {{Twitter Field Experiment}}},
  author = {Mosleh, Mohsen and Martel, Cameron and Eckles, Dean and Rand, David Gertler},
  year = {2020},
  month = oct,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/ykh5t},
  abstract = {Americans are much more likely to be socially connected to co-partisans, both in daily life and on social media. But this observation does not necessarily mean that shared partisanship per se drives social tie formation, because partisanship is confounded with many other factors. Here, we test the causal effect of shared partisanship on the formation of social ties in a field experiment on Twitter. We created bot accounts that self-identified as people who favored the Democratic or Republican party, and that varied in the strength of that identification. We then randomly assigned 842 Twitter users to be followed by one of our accounts. Users were roughly three times more likely to reciprocally follow-back bots whose partisanship matched their own, and this was true regardless of the bot's strength of identification. Interestingly, there was no partisan asymmetry in this preferential follow-back behavior: Democrats and Republicans alike were much more likely to reciprocate follows from co-partisans. These results demonstrate a strong causal effect of shared partisanship on the formation of social ties in an ecologically valid field setting, and have important implications for political psychology, social media, and the politically polarized state of the American public.},
  file = {/home/aleksandar/Zotero/storage/JE2US5YY/Mosleh et al. - 2020 - Shared Partisanship Dramatically Increases Social .pdf},
  language = {en},
  type = {Preprint}
}

@techreport{mottusDescriptivePredictiveExplanatory2020,
  title = {Descriptive, Predictive and Explanatory Personality Research: {{Different}} Goals, Different Approaches, but a Shared Need to Move beyond the {{Big Few}} Traits},
  shorttitle = {Descriptive, Predictive and Explanatory Personality Research},
  author = {M{\~o}ttus, Ren{\'e} and Wood, Dustin and Condon, David M and Back, Mitja and Baumert, Anna and Costantini, Giulio and Epskamp, Sacha and Greiff, Samuel and Johnson, Wendy and Lukaszewski, Aaron and Murray, Aja Louise and Revelle, William and Wright, Aidan G.C. and Yarkoni, Tal and Ziegler, Matthias and Zimmermann, Johannes},
  year = {2020},
  month = nov,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/hvk5p},
  abstract = {We argue that it is useful to distinguish between three key goals of personality science \textendash{} description, prediction and explanation \textendash{} and that attaining them often requires different priorities and methodological approaches. We put forward specific recommendations such as publishing findings with minimum a priori aggregation and exploring the limits of predictive models without being constrained by parsimony and intuitiveness but instead maximising out-of-sample predictive accuracy. We argue that naturally-occurring variance in many decontextualized and multi-determined constructs that interest personality scientists may not have individual causes, at least as this term is generally understood and in ways that are human-interpretable, never mind intervenable. If so, useful explanations are narratives that summarize many pieces of descriptive findings rather than models that target individual cause-effect associations. By meticulously studying specific and contextualized behaviours, thoughts, feelings and goals, however, individual causes of variance may ultimately be identifiable, although such causal explanations will likely be far more complex, phenomenon-specific and personspecific than anticipated thus far. Progress in all three areas \textendash{} description, prediction, and explanation \textendash{} requires higherdimensional models than the currently-dominant ``Big Few'' and supplementing subjective trait-ratings with alternative sources of information such as informant-reports and behavioural measurements. Developing a new generation of psychometric tools thus provides many immediate research opportunities.},
  file = {/home/aleksandar/Zotero/storage/4Y2PVCGL/Mõttus et al_2020_Descriptive, predictive and explanatory personality research.pdf},
  keywords = {_tablet},
  language = {en},
  type = {Preprint}
}

@techreport{moulderTangleMetricQuantifying2019,
  title = {Tangle: {{A Metric}} for {{Quantifying Complexity}} and {{Erratic Behavior}} in {{Short Time Series}}},
  shorttitle = {Tangle},
  author = {Moulder, Robert Glenn and Daniel, Katharine E and Teachman, Bethany and Boker, Steven M.},
  year = {2019},
  month = sep,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/23csa},
  abstract = {Background:Temporal complexity refers to qualities of a time series that are emergent, erratic, or not easily described by linear processes. Quantifying temporal complexity within a system is key to understanding the time based dynamics of said system. However, many current methods of complexity quantification are not widely used due to their technical difficulty, computational intensity, or large number of required data samples. These requirements impede the study of complexity in areas of research such as behavioral science and mental health. A method is presented, tangle, which overcomes these difficulties and allows for complexity quantification in relatively short time series, such as those typically obtained by these research areas. Tangle is a measure of how dissimilar a given process is from simple periodic motion. Methods:Tangle is derived from an iterative scaling and untangling procedure. This procedure relies on the use of a 3-dimensional time delay embedding of a 1-dimensional time series. This embedding is then iteratively scaled and premultiplied by a modified upshift matrix until a convergence criterion is reached. Results:Systems studied are: (A) random noise, (B) a Lorenz attractor, (C) a Rossler attractor, (D) a Hindmarsh-Rose neuron model, and (E) a noisy sine wave. An example application of tangle for studying mental health is given using emotional stability and anxiety time series data obtained from 65 socially anxious participants over a five-week period. Simulation results show tangle is able to distinguish between different complex temporal systems in time series with as few as 50 samples. Conclusions:Tangle shows promise as a reliable quantification of irregular behavior of a time series. Unlike many other complexity quantification metrics, tangle is technically simple to implement and is able to uncover meaningful information about time series derived from behavioral and mental health research studies. Future work should focus on comparing tangle to other temporal complexity metrics and seek to optimize convergence.},
  language = {en},
  type = {Preprint}
}

@inproceedings{muiComputationalModelTrust2002,
  title = {A Computational Model of Trust and Reputation},
  booktitle = {Proceedings of the 35th {{Annual Hawaii International Conference}} on {{System Sciences}}},
  author = {Mui, L. and Mohtashemi, M. and Halberstadt, A.},
  year = {2002},
  month = jan,
  pages = {2431--2439},
  doi = {10.1109/HICSS.2002.994181},
  abstract = {Despite their many advantages, e-businesses lag behind brick and mortar businesses in several fundamental respects. This paper concerns one of these: relationships based on trust and reputation. Recent studies on simple reputation systems for e-Businesses such as eBay have pointed to the importance of such rating systems for deterring moral hazard and encouraging trusting interactions. However, despite numerous studies on trust and reputation systems, few have taken studies across disciplines to provide an integrated account of these concepts and their relationships. This paper first surveys existing literatures on trust, reputation and a related concept: reciprocity. Based on sociological and biological understandings of these concepts, a computational model is proposed. This model can be implemented in a real system to consistently calculate agents' trust and reputation scores.},
  file = {/home/aleksandar/Zotero/storage/PHD7IDW8/Mui et al_2002_A computational model of trust and reputation.pdf;/home/aleksandar/Zotero/storage/KGP5ZHSE/994181.html}
}

@techreport{mukerjeeMeasuringSelectiveExposure2020,
  title = {Measuring {{Selective Exposure}}: {{A Systematic Comparison}} of {{Community Detection Algorithms}} in {{Coexposure Networks}}},
  shorttitle = {Measuring {{Selective Exposure}}},
  author = {Mukerjee, Subhayan},
  year = {2020},
  month = nov,
  institution = {{Open Science Framework}},
  doi = {10.31219/osf.io/3dyur},
  abstract = {The use of network analytic techniques for understanding audience fragmentation and selective exposure to information has received substantial scholarly attention in recent years. In this paper, I propose a formal mathematical model for audience co-exposure networks by simulating audience behavior in an artificial media environment. I show how a variety of synthetic audience overlap networks can be generated by tuning specific  parameters, that control various aspects of the media environment and  individual behavior. I then use a variety of community detection algorithms to characterize the level of audience fragmentation in these synthetic networks and compare their performances for different combinations of the model parameters. Finally, I validate these findings using a novel empirical data-set of actual large-scale browsing behavior and demonstrate the model's utility in informing future analytical choices.},
  file = {/home/aleksandar/Zotero/storage/AVWLK9YY/Mukerjee - 2020 - Measuring Selective Exposure A Systematic Compari.pdf},
  language = {en},
  type = {Preprint}
}

@article{mulderBFpackFlexibleBayes2019,
  title = {{{BFpack}}: {{Flexible Bayes Factor Testing}} of {{Scientific Theories}} in {{R}}},
  shorttitle = {{{BFpack}}},
  author = {Mulder, Joris and Gu, Xin and {Olsson-Collentine}, Anton and Tomarken, Andrew and {B{\"o}ing-Messing}, Florian and Hoijtink, Herbert and Meijerink, Marlyne and Williams, Donald R. and Menke, Janosch and Fox, Jean-Paul and Rosseel, Yves and Wagenmakers, Eric-Jan and {van Lissa}, Caspar},
  year = {