text:
obtained in these networks. The phase transitions are discussed, and the phase diagrams are obtained via Monte Carlo simulations and finite-size analyzing. We find that as the addition of links increases the phase transition temperature increases and the transition competing probability of tricritical point decreases. For the competition of the two dynamics, ferromagnetic to anti-ferromagnetic phase transitions and the critical endpoints are found in the small-world networks.{$<$}/p{$>$}},
  journal = {Open Physics}
}

@article{liuNonequilibriumPhaseTransitionsa,
  title = {Non-Equilibrium {{Phase Transitions}} in {{2D Small}}-{{World Networks}}: {{Competing Dynamics}}},
  author = {Liu, Wei and Yan, Zhengxin and Zhou, Gaoliang},
  doi = {10.1515/phys-2019-0001},
  abstract = {{$<$}p{$>$}This article offers a detailed analysis of the Ising model in 2D small-world networks with competing Glauber and Kawasaki dynamics. The non-equilibrium stationary state phase transitions are obtained in these networks. The phase transitions are discussed, and the phase diagrams are obtained via Monte Carlo simulations and finite-size analyzing. We find that as the addition of links increases the phase transition temperature increases and the transition competing probability of tricritical point decreases. For the competition of the two dynamics, ferromagnetic to anti-ferromagnetic phase transitions and the critical endpoints are found in the small-world networks.{$<$}/p{$>$}},
  journal = {Open Physics}
}

@article{liuOpinionWalkEfficientSolution2017,
  title = {{{OpinionWalk}}: {{An}} Efficient Solution to Massive Trust Assessment in Online Social Networks},
  shorttitle = {{{OpinionWalk}}},
  author = {Liu, G. and Chen, Qi and Yang, Q. and Zhu, B. and Wang, H. and Wang, W.},
  year = {2017},
  doi = {10.1109/INFOCOM.2017.8057106},
  abstract = {Massive trust assessment (MTA) in an Online Social Network (OSN), i.e., computing the trustworthiness of all users in the network, is crucial in various OSN-related applications. Existing solutions are either too slow or inaccurate in addressing the MTA problem. We propose the OpinionWalk algorithm that accurately and efficiently conducts MTA in an OSN. OpinionWalk models trust by the Dirichlet distribution and uses a matrix to represent the direct trust relations among users. From the perspective of a user, other users' trustworthiness are stored in a column vector that is iteratively updated when the algorithm ``walks'' through the network, in a breadth-first search manner. We identify the overlapping subproblems property in MTA and prove OpinionWalk is a more efficient solution. The accuracy and execution time of OpinionWalk are evaluated and compared to benchmark algorithms including EigenTrust, TrustRank, MoleTrust, TidalTrust and AssessTrust, using two real-world datasets (Advogato and Pretty Good Privacy). Experimental results indicate that OpinionWalk is an efficient and accurate solution to MTA, compared to previous algorithms.},
  journal = {IEEE INFOCOM 2017 - IEEE Conference on Computer Communications}
}

@article{liuPositiveCorrelationStrategy,
  title = {Positive Correlation between Strategy Persistence and Teaching Ability Promotes Cooperation in Evolutionary {{Prisoner}}'s {{Dilemma}} Games},
  author = {Liu, Danna and Huang, Changwei and Dai, Qionglin and Li, Haihong},
  doi = {S0378437119300421},
  abstract = {{$<$}p{$>$}Publication date: Available online 16 January 2019{$<$}/p{$><$}p{$>$}\textbf{Source:} Physica A: Statistical Mechanics and its Applications{$<$}/p{$><$}p{$>$}Author(s): Danna Liu, Changwei Huang, Qionglin Dai, Haihong Li{$<$}/p{$><$}h5{$>$}Abstract{$<$}/h5{$><$}div{$><$}p{$>$}Strategy persistence and heterogeneous teaching ability are two factors that play important roles in promoting cooperation. In this work, we simultaneously take the strategy persistence and heterogeneous teaching ability into account in evolutionary prisoner's dilemma games. We introduce the correlation between strategy persistence ({$<$}math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e693" altimg="si70.gif" class="math"{$><$}mi{$>\tau<$}/mi{$><$}/math{$>$}) and teaching ability ({$<$}math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e698" altimg="si74.gif" class="math"{$><$}mi{$>\omega<$}/mi{$><$}/math{$>$}) by partitioning a population into two groups. In the positive (negative) correlation scheme, individuals in one group are assigned with large {$<$}math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e703" altimg="si70.gif" class="math"{$><$}mi{$>\tau<$}/mi{$><$}/math{$>$} and {$<$}math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e709" altimg="si71.gif" class="math"{$><$}mi{$>\omega<$}/mi{$><$}mo{$>$}={$<$}/mo{$><$}mn{$>$}1{$<$}/mn{$><$}/math{$>$} (reduced teaching ability {$<$}math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e718" altimg="si5.gif" class="math"{$><$}mi{$>\omega<$}/mi{$><$}mo{$><<$}/mo{$><$}mn{$>$}1{$<$}/mn{$><$}/math{$>$}) and those in the other with {$<$}math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e727" altimg="si75.gif" class="math"{$><$}mi{$>\tau<$}/mi{$><$}mo{$>$}={$<$}/mo{$><$}mn{$>$}0{$<$}/mn{$><$}/math{$>$} and {$<$}math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e736" altimg="si5.gif" class="math"{$><$}mi{$>\omega<$}/mi{$><$}mo{$><<$}/mo{$><$}mn{$>$}1{$<$}/mn{$><$}/math{$>$} ({$<$}math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e746" altimg="si71.gif" class="math"{$><$}mi{$>\omega<$}/mi{$><$}mo{$>$}={$<$}/mo{$><$}mn{$>$}1{$<$}/mn{$><$}/math{$>$}). The simulation results show that, compared to negative correlation, positive correlation between strategy persistence and teaching ability can greatly enhance cooperation and result in a much higher cooperation level. Furthermore, we show that the finding is robust with respect to reduced teaching ability {$<$}math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e755" altimg="si74.gif" class="math"{$><$}mi{$>\omega<$}/mi{$><$}/math{$>$}, initial cooperator fraction {$<$}math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e760" altimg="si170.gif" class="math"{$><$}msub{$><$}mrow{$><$}mi{$>$}f{$<$}/mi{$><$}/mrow{$><$}mrow{$><$}mn{$>$}0{$<$}/mn{$><$}/mrow{$><$}/msub{$><$}/math{$>$}, amplitude of noise {$<$}math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e770" altimg="si179.gif" class="math"{$><$}mi{$>$}K{$<$}/mi{$><$}/math{$>$} andunderlying network topology.{$<$}/p{$><$}/div{$>$}},
  journal = {Physica A: Statistical Mechanics and its Applications}
}

@article{liuPositiveCorrelationStrategya,
  title = {Positive Correlation between Strategy Persistence and Teaching Ability Promotes Cooperation in Evolutionary {{Prisoner}}'s {{Dilemma}} Games},
  author = {Liu, Danna and Huang, Changwei and Dai, Qionglin and Li, Haihong},
  doi = {S0378437119300421},
  abstract = {{$<$}p{$>$}Publication date: Available online 16 January 2019{$<$}/p{$><$}p{$>$}\textbf{Source:} Physica A: Statistical Mechanics and its Applications{$<$}/p{$><$}p{$>$}Author(s): Danna Liu, Changwei Huang, Qionglin Dai, Haihong Li{$<$}/p{$><$}h5{$>$}Abstract{$<$}/h5{$><$}div{$><$}p{$>$}Strategy persistence and heterogeneous teaching ability are two factors that play important roles in promoting cooperation. In this work, we simultaneously take the strategy persistence and heterogeneous teaching ability into account in evolutionary prisoner's dilemma games. We introduce the correlation between strategy persistence ({$<$}math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e693" altimg="si70.gif" class="math"{$><$}mi{$>\tau<$}/mi{$><$}/math{$>$}) and teaching ability ({$<$}math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e698" altimg="si74.gif" class="math"{$><$}mi{$>\omega<$}/mi{$><$}/math{$>$}) by partitioning a population into two groups. In the positive (negative) correlation scheme, individuals in one group are assigned with large {$<$}math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e703" altimg="si70.gif" class="math"{$><$}mi{$>\tau<$}/mi{$><$}/math{$>$} and {$<$}math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e709" altimg="si71.gif" class="math"{$><$}mi{$>\omega<$}/mi{$><$}mo{$>$}={$<$}/mo{$><$}mn{$>$}1{$<$}/mn{$><$}/math{$>$} (reduced teaching ability {$<$}math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e718" altimg="si5.gif" class="math"{$><$}mi{$>\omega<$}/mi{$><$}mo{$><<$}/mo{$><$}mn{$>$}1{$<$}/mn{$><$}/math{$>$}) and those in the other with {$<$}math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e727" altimg="si75.gif" class="math"{$><$}mi{$>\tau<$}/mi{$><$}mo{$>$}={$<$}/mo{$><$}mn{$>$}0{$<$}/mn{$><$}/math{$>$} and {$<$}math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e736" altimg="si5.gif" class="math"{$><$}mi{$>\omega<$}/mi{$><$}mo{$><<$}/mo{$><$}mn{$>$}1{$<$}/mn{$><$}/math{$>$} ({$<$}math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e746" altimg="si71.gif" class="math"{$><$}mi{$>\omega<$}/mi{$><$}mo{$>$}={$<$}/mo{$><$}mn{$>$}1{$<$}/mn{$><$}/math{$>$}). The simulation results show that, compared to negative correlation, positive correlation between strategy persistence and teaching ability can greatly enhance cooperation and result in a much higher cooperation level. Furthermore, we show that the finding is robust with respect to reduced teaching ability {$<$}math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e755" altimg="si74.gif" class="math"{$><$}mi{$>\omega<$}/mi{$><$}/math{$>$}, initial cooperator fraction {$<$}math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e760" altimg="si170.gif" class="math"{$><$}msub{$><$}mrow{$><$}mi{$>$}f{$<$}/mi{$><$}/mrow{$><$}mrow{$><$}mn{$>$}0{$<$}/mn{$><$}/mrow{$><$}/msub{$><$}/math{$>$}, amplitude of noise {$<$}math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e770" altimg="si179.gif" class="math"{$><$}mi{$>$}K{$<$}/mi{$><$}/math{$>$} andunderlying network topology.{$<$}/p{$><$}/div{$>$}},
  journal = {Physica A: Statistical Mechanics and its Applications}
}

@article{liuPromotionCooperationEvolutionary,
  title = {Promotion of Cooperation in Evolutionary Game Dynamics under Asymmetric Information},
  author = {Liu, Xuesong and Pan, Qiuhui and He, Mingfeng and Liu, Aizhi},
  doi = {S0378437119300329},
  abstract = {{$<$}p{$>$}Publication date: Available online 16 January 2019{$<$}/p{$><$}p{$>$}\textbf{Source:} Physica A: Statistical Mechanics and its Applications{$<$}/p{$><$}p{$>$}Author(s): Xuesong Liu, Qiuhui Pan, Mingfeng He, Aizhi Liu{$<$}/p{$><$}h5{$>$}Abstract{$<$}/h5{$><$}div{$><$}p{$>$}The emergence and abundance of cooperation in the population of selfish individuals is a challenging problem in evolutionary dynamics. Here we propose two asymmetric information systems in which individuals adopt two different rules to update their strategies. One of the stochastic evolutionary models is mixed by aspiration-driven rule and local information-driven rule, while the other model is mixed by local rule and imitation-driven rule. The proposed heterogeneous systems promote at least one kind of individuals' cooperation. Compared with the standard imitation dynamics and local dynamics, there is always a non-zero probability to support the formation of cooperation in the~system with the aspiration and other rules. Furthermore, the more aspiration-driven individuals lead to higher fraction of local-driven cooperators. In the other case, the invasion of individuals who use local information is conductive to promoting the imitation-driven ones' cooperation. In both heterogeneous systems, we find that the individuals who use less information are more likely to cooperate than the ones who use higher information.{$<$}/p{$><$}/div{$>$}},
  journal = {Physica A: Statistical Mechanics and its Applications}
}

@article{liuPromotionCooperationEvolutionarya,
  title = {Promotion of Cooperation in Evolutionary Game Dynamics under Asymmetric Information},
  author = {Liu, Xuesong and Pan, Qiuhui and He, Mingfeng and Liu, Aizhi},
  doi = {S0378437119300329},
  abstract = {{$<$}p{$>$}Publication date: Available online 16 January 2019{$<$}/p{$><$}p{$>$}\textbf{Source:} Physica A: Statistical Mechanics and its Applications{$<$}/p{$><$}p{$>$}Author(s): Xuesong Liu, Qiuhui Pan, Mingfeng He, Aizhi Liu{$<$}/p{$><$}h5{$>$}Abstract{$<$}/h5{$><$}div{$><$}p{$>$}The emergence and abundance of cooperation in the population of selfish individuals is a challenging problem in evolutionary dynamics. Here we propose two asymmetric information systems in which individuals adopt two different rules to update their strategies. One of the stochastic evolutionary models is mixed by aspiration-driven rule and local information-driven rule, while the other model is mixed by local rule and imitation-driven rule. The proposed heterogeneous systems promote at least one kind of individuals' cooperation. Compared with the standard imitation dynamics and local dynamics, there is always a non-zero probability to support the formation of cooperation in the~system with the aspiration and other rules. Furthermore, the more aspiration-driven individuals lead to higher fraction of local-driven cooperators. In the other case, the invasion of individuals who use local information is conductive to promoting the imitation-driven ones' cooperation. In both heterogeneous systems, we find that the individuals who use less information are more likely to cooperate than the ones who use higher information.{$<$}/p{$><$}/div{$>$}},
  journal = {Physica A: Statistical Mechanics and its Applications}
}

@article{liuStatisticalMethodsNetworks,
  title = {Statistical {{Methods}} for {{Networks}} with {{Node Covariates}}},
  author = {Liu, Yumu},
  pages = {127},
  file = {/home/aleksandar/Zotero/storage/3RUVH5BU/Liu - Statistical Methods for Networks with Node Covaria.pdf},
  language = {en}
}

@article{liuSTCCDSemanticTrajectory2020,
  title = {{{STCCD}}: {{Semantic Trajectory Clustering}} Based on {{Community Detection}} in {{Networks}}},
  shorttitle = {{{STCCD}}},
  author = {Liu, Caihong and Guo, Chonghui},
  year = {2020},
  month = jun,
  pages = {113689},
  issn = {0957-4174},
  doi = {10.1016/j.eswa.2020.113689},
  abstract = {Most of traditional trajectory clustering algorithms often cluster similar trajectories from a temporal or spatial perspective. One weak point is that the semantic relationship between the trajectories is ignored. In some cases, trajectories with spatio-temporal similarities may be semantically related, and the negligence of semantic information may result in unreasonable trajectory clustering results. In addition, the existing semantic trajectory clustering algorithms only consider the local semantic relationship between adjacent spatio-temporal trajectories, and the overall global semantic relationship between trajectories is still unknown. Considering the disadvantages of the current trajectory clustering methods, we proposed a novel algorithm for semantic trajectory clustering based on community detection (STCCD) in networks, which can better measure the semantic similarity of trajectories and capture global relationship among trajectories from the perspective of the network, and can get better trajectory clustering results compared to some traditional and recently proposed methods. Experimental results demonstrate that the proposed method can effectively mine the trajectory clustering information and related knowledge from the semantic trajectory data.},
  file = {/home/aleksandar/Zotero/storage/698ILIHK/S0957417420305133.html},
  journal = {Expert Systems with Applications},
  language = {en}
}

@article{lizardoCulturalObjectsPrisms2016,
  title = {Cultural {{Objects}} as {{Prisms}}: {{Perceived Audience Composition}} of {{Musical Genres}} as a {{Resource}} for {{Symbolic Exclusion}}},
  shorttitle = {Cultural {{Objects}} as {{Prisms}}},
  author = {Lizardo, Omar and Skiles, Sara},
  year = {2016},
  month = aug,
  volume = {2},
  pages = {237802311664169},
  issn = {2378-0231, 2378-0231},
  doi = {10.1177/2378023116641695},
  journal = {Socius: Sociological Research for a Dynamic World},
  language = {en}
}

@article{lizardoSkillsToolkitsContexts2010,
  title = {Skills, Toolkits, Contexts and Institutions: {{Clarifying}} the Relationship between Different Approaches to Cognition in Cultural Sociology},
  shorttitle = {Skills, Toolkits, Contexts and Institutions},
  author = {Lizardo, Omar and Strand, Michael},
  year = {2010},
  month = apr,
  volume = {38},
  pages = {205--228},
  issn = {0304422X},
  doi = {10.1016/j.poetic.2009.11.003},
  journal = {Poetics},
  language = {en},
  number = {2}
}

@book{loehlinLatentVariableModels2004,
  title = {Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis},
  shorttitle = {Latent Variable Models},
  author = {Loehlin, John C.},
  year = {2004},
  edition = {4th ed},
  publisher = {{L. Erlbaum Associates}},
  address = {{Mahwah, N.J}},
  isbn = {978-0-8058-4909-7 978-0-8058-4910-3},
  lccn = {QA278.6 .L64 2004}
}

@incollection{loganIdentifyingEthnicNeighborhoods2004,
  title = {Identifying Ethnic Neighborhoods with Census Data},
  booktitle = {Spatially Integrated Social Science},
  author = {Logan, J. R. and Zhang, W.},
  editor = {Goodchild, Michael F. and Janelle, Donald G.},
  year = {2004},
  pages = {113--126},
  publisher = {{Oxford University Press}},
  address = {{Oxford}}
}

@techreport{lohDisentanglingIndirectEffects2019,
  title = {Disentangling Indirect Effects through Multiple Mediators Whose Causal Structure Is Unknown},
  author = {Loh, Wen Wei and Moerkerke, Beatrijs and Loeys, Tom and Vansteelandt, Stijn},
  year = {2019},
  month = nov,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/3q4hg},
  abstract = {When there are multiple mediators on the causal pathway from a treatment to an outcome, path analysis is commonly used to disentangle the specific indirect effects transmitted along causal path(s) through each distinct mediator. However, fine-grained decompositions of specific indirect or mediated effects along separate paths are only valid under stringent assumptions, such as a correctly specified causal structure of the mediators, and no unobserved confounding of the mediators. In this article, we introduce a new type of direct and indirect effects for multiple mediators, called interventional effects, from the causal inference and epidemiology literature. While the framework for interventional direct and indirect effects can accommodate nonlinear models for the means of the mediators and/or the outcome in general, we will focus on a particular class of linear models widely used for multiple mediation analysis. We demonstrate how the interventional indirect effects through each distinct mediator can be unbiasedly estimated using prevalent path analysis methods within the linear structural equation modeling (SEM) framework. The estimators do not require specifying the directions of the causal effects between the mediators, and are unbiased (in large samples) even when the mediators share hidden or unobserved common causes. The estimation method is utilized to assess the effect of political inclusion on political prejudice that is possibly mediated by six distinct mediators.},
  type = {Preprint}
}

@article{loiaconoDoesCommunitySocial2018,
  title = {Does Community Social Embeddedness Promote Generalized Trust? {{An}} Experimental Test of the Spillover Effect},
  shorttitle = {Does Community Social Embeddedness Promote Generalized Trust?},
  author = {Lo Iacono, Sergio},
  year = {2018},
  month = jul,
  volume = {73},
  pages = {126--145},
  issn = {0049-089X},
  doi = {10.1016/j.ssresearch.2018.03.001},
  abstract = {Despite the theoretical relevance attributed to the spillover effect, little empirical research has focused on testing its causal validity. Addressing this gap in the literature, I propose a novel experimental design to test if the overall density of social links in a community promotes trustworthy and trusting behaviors with absolute strangers. Controlling for social integration (i.e. the individual number of social connections), I found that density fosters higher levels of trust. In particular, results show that people in denser communities are more likely to trust their unknown fellow citizens, encouraging isolated subjects to engage with strangers. However, evidence did not support the idea that community social embeddedness causes an increase of trustworthiness, indicating that the spillover effect works only with respect to trust.},
  file = {/home/aleksandar/Dropbox/Zotero/Lo Iacono/Lo Iacono_2018_Does community social embeddedness promote generalized trust.pdf;/home/aleksandar/Zotero/storage/IC7GU9AY/S0049089X17304398.html},
  journal = {Social Science Research},
  language = {en}
}

@article{longUsingPredictionsMarginal2018,
  title = {Using {{Predictions}} and {{Marginal Effects}} to {{Compare Groups}} in {{Regression Models}} for {{Binary Outcomes}}},
  author = {Long, J. Scott and Mustillo, Sarah A.},
  year = {2018},
  month = oct,
  pages = {0049124118799374},
  issn = {0049-1241},
  doi = {10.1177/0049124118799374},
  abstract = {Methods for group comparisons using predicted probabilities and marginal effects on probabilities are developed for regression models for binary outcomes. Unlike approaches based on the comparison of regression coefficients across groups, the methods we propose are unaffected by the scalar identification of the coefficients and are expressed in the natural metric of the outcome probability. While we develop our approach using binary logit with two groups, we consider how our interpretive framework can be used with a broad class of regression models and can be extended to any number of groups.},
  journal = {Sociological Methods \& Research},
  language = {en}
}

@article{lookSupportDemocracyAuthoritarianism,
  title = {Support for Democracy under Authoritarianism:},
  author = {Look, Emily},
  pages = {239},
  file = {/home/aleksandar/Dropbox/Zotero/Look/Look_Support for democracy under authoritarianism.pdf},
  language = {en}
}

@article{lorenzContinuousOpinionDynamics2007,
  title = {Continuous Opinion Dynamics under Bounded Confidence: A Survey},
  shorttitle = {Continuous Opinion Dynamics under Bounded Confidence},
  author = {Lorenz, Jan},
  year = {2007},
  month = dec,
  volume = {18},
  pages = {1819--1838},
  publisher = {{World Scientific Publishing Co.}},
  issn = {0129-1831},
  doi = {10.1142/S0129183107011789},
  abstract = {Models of continuous opinion dynamics under bounded confidence have been presented independently by Krause and Hegselmann and by Deffuant et al. in 2000. They have raised a fair amount of attention in the communities of social simulation, sociophysics and complexity science. The researchers working on it come from disciplines such as physics, mathematics, computer science, social psychology and philosophy.In these models agents hold continuous opinions which they can gradually adjust if they hear the opinions of others. The idea of bounded confidence is that agents only interact if they are close in opinion to each other. Usually, the models are analyzed with agent-based simulations in a Monte Carlo style, but they can also be reformulated on the agent's density in the opinion space in a master equation style. The contribution of this survey is fourfold. First, it will present the agent-based and density-based modeling frameworks including the cases of multidimensional opinions and heterogeneous bounds of confidence. Second, it will give the bifurcation diagrams of cluster configuration in the homogeneous model with uniformly distributed initial opinions. Third, it will review the several extensions and the evolving phenomena which have been studied so far, and fourth it will state some open questions.},
  file = {/home/aleksandar/Documents/Zotero/Lorenz/Lorenz_2007_Continuous opinion dynamics under bounded confidence.pdf;/home/aleksandar/Zotero/storage/DQH9XKKT/S0129183107011789.html},
  journal = {International Journal of Modern Physics C},
  number = {12}
}

@techreport{lorenzIndividualAttitudeChange2020,
  title = {Individual Attitude Change and Societal Dynamics: {{Computational}} Experiments with Psychological Theories},
  shorttitle = {Individual Attitude Change and Societal Dynamics},
  author = {Lorenz, Jan and Neumann, Martin and Schr{\"o}der, Tobia},
  year = {2020},
  month = jan,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/ebfvr},
  abstract = {We present an agent-based model for studying the societal implications of theories of attitude change in psychological theory. For experimenting with theories, various psychological theories of persuasive communication are implemented. The model allows for investigating the effects of contagion and assimilation, motivated cognition, polarity, source credibility, and idiosyncratic attitude formation. The simulations show that different theories produce different characteristic macro-level patterns. Central mechanisms for generating consensus can be contagion and assimilation. However, the former generates a radicalized consensus. Motivated cognition tends to cause societal polarization of attitudes. Polarity and source credibility have comparatively little effect on the societal distribution of attitudes. We discuss how the simulations provide a bridge between micro-level psychological theories and the aggregated macro level studied by sociology. This, in turn, enables complementing experimental studies by new types of evidence for evaluating social psychological theory.},
  language = {en},
  type = {Preprint}
}

@article{lorrainStructuralEquivalenceIndividuals1971,
  title = {Structural Equivalence of Individuals in Social Networks},
  author = {Lorrain, Fran{\c c}ois and White, Harrison C.},
  year = {1971},
  month = jan,
  volume = {1},
  pages = {49--80},
  issn = {0022-250X, 1545-5874},
  doi = {10.1080/0022250X.1971.9989788},
  journal = {The Journal of Mathematical Sociology},
  language = {en},
  number = {1}
}

@article{louisVolatilityCollectiveAction,
  title = {The {{Volatility}} of {{Collective Action}}: {{Theoretical Analysis}} and {{Empirical Data}}},
  shorttitle = {The {{Volatility}} of {{Collective Action}}},
  author = {Louis, Winnifred and Thomas, Emma and McGarty, Craig and Lizzio-Wilson, Morgana and Amiot, Catherine and Moghaddam, Fathali},
  volume = {n/a},
  issn = {1467-9221},
  doi = {10.1111/pops.12671},
  abstract = {Collective action is volatile: characterized by swift, unexpected changes in intensity, target, and forms. We conduct a detailed exploration of four reasons that these changes occur. First, action is about identities which are fluid, contested, and multifaceted. As the content of groups' identities change, so do the specific norms for the identities. Second, social movements adopt new tactics, or forms of collective action. Tactical changes may arise from changes in identity, but also changes in the target or opponent groups, and changes in the relationships with targets and with other actors. Factions or wings of a group in conflict may in turn form identities based on opposition or support for differing tactics. Third, social movements change because participant motivation ebbs, surges, and also changes in quality (e.g., becoming more subjectively autonomous, or self-determined). Finally, political social change occurs within socio-political structures; these structures implicate higher-level norms, which both constrain and emerge from actions (e.g., state openness or repression). Our analysis presents idealized and descriptive models of these relationships, and a new model to examine tactical changes empirically, the DIME model. This model highlights that collective actors can Disidentify after failure (giving up and walking away); they can Innovate, or try something new; and they can commit harder, convinced that they are right, with increased moral urgency (Moralization) and redoubled efforts (Energization).},
  annotation = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/pops.12671},
  copyright = {\textcopyright{} 2020 International Society of Political Psychology},
  file = {/home/aleksandar/Zotero/storage/ZW6CBIA9/pops.html},
  journal = {Political Psychology},
  language = {en},
  number = {n/a}
}

@inproceedings{louPredictingRobustnessUndirected2020,
  title = {Predicting the {{Robustness}} of {{Undirected Network Controllability}}},
  booktitle = {2020 39th {{Chinese Control Conference}} ({{CCC}})},
  author = {Lou, Yang and He, Yaodong and Wang, Lin and Tsang, Kim Fung and Chen, Guanrong},
  year = {2020},
  month = jul,
  pages = {4550--4553},
  issn = {1934-1768},
  doi = {10.23919/CCC50068.2020.9189097},
  abstract = {Robustness of the network controllability reflects how well a networked system can maintain its controllability against destructive attacks. The measure of the network controllability robustness is quantified by a sequence of values that record the remaining controllability of the network after a sequence of node-removal or edge-removal attacks. Traditionally, the controllability robustness is determined by attack simulations, which is computationally time consuming. In this paper, an improved method for predicting the controllability robustness of undirected networks is developed based on machine learning using a convolutional neural network. This approach is motivated by the following observations: 1) there is no clear correlation between the topological features and the controllability robustness of a general undirected network, 2) the adjacency matrix of a network can be represented as a gray-scale image, 3) the convolutional neural network technique has proved successful in image processing without human intervention. In the new framework, preprocessing and filtering are embedded, and a sufficiently large number of training datasets generated by simulations are used to train several convolutional neural networks for classification and prediction, respectively. Extensive experimental studies were carried out, which demonstrate that the proposed framework for predicting the controllability robustness of undirected networks is more accurate and reliable than the conventional single convolutional neural network predictor.},
  file = {/home/aleksandar/Zotero/storage/269AI2XV/9189097.html}
}

@article{lovatoDistributedConsentIts2020,
  title = {Distributed Consent and Its Impact on Privacy and Observability in Social Networks},
  author = {Lovato, Juniper and Allard, Antoine and Harp, Randall and {H{\'e}bert-Dufresne}, Laurent},
  year = {2020},
  month = jun,
  abstract = {Personal data is not discrete in socially-networked digital environments. A single user who consents to allow access to their own profile can thereby expose the personal data of their network connections to non-consented access. The traditional (informed individual) consent model is therefore not appropriate in online social networks where informed consent may not be possible for all users affected by data processing and where information is shared and distributed across many nodes. Here, we introduce a model of "distributed consent" where individuals and groups can coordinate by giving consent conditional on that of their network connections. We model the impact of distributed consent on the observability of social networks and find that relatively low adoption of even the simplest formulation of distributed consent would allow macroscopic subsets of online networks to preserve their connectivity and privacy. Distributed consent is of course not a silver bullet, since it does not follow data as it flows in and out of the system, but it is one of the most straightforward non-traditional models to implement and it better accommodates the fuzzy, distributed nature of online data.},
  archivePrefix = {arXiv},
  eprint = {2006.16140},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Dropbox/Zotero/Lovato et al/Lovato et al_2020_Distributed consent and its impact on privacy and observability in social.pdf;/home/aleksandar/Zotero/storage/2RTKZP6L/2006.html},
  journal = {arXiv:2006.16140 [physics]},
  primaryClass = {physics}
}

@article{lovelessWhenYouWin2020,
  title = {When {{You Win}}, {{Nothing Hurts}}: {{The Durability}} of {{Electoral Salience}} on {{Individuals}}' {{Satisfaction}} with {{Democracy}}},
  shorttitle = {When {{You Win}}, {{Nothing Hurts}}},
  author = {Loveless, Matthew},
  year = {2020},
  month = may,
  pages = {0032321720910356},
  publisher = {{SAGE Publications Ltd}},
  issn = {0032-3217},
  doi = {10.1177/0032321720910356},
  abstract = {There is a substantial literature on the impact of having voting for an electorally victorious party on individual voters' satisfaction with democracy. Yet, there have been few evaluations as to temporally salient are elections to the satisfaction levels for those who voted for a ``winning'' party and those who voted for a ``losing'' party. Using rounds 1\textendash 8 of the European Social Surveys, I find evidence from 92 elections in 27 European countries that both the levels of and the difference between satisfaction levels of ``winners'' and ``losers'' do not attenuate quickly but rather last almost 5\,years. That is, it appears that ``winners'' are more satisfied with democracy and stay that way. While this confirms earlier, smaller studies, the absence of a causal connection between the time from election and satisfaction levels poses a significant challenge to the current literature about the electoral mechanism of this relationship.},
  journal = {Political Studies},
  language = {en}
}

@article{lovricConflictsBayesianStatistics2020,
  title = {Conflicts in {{Bayesian Statistics Between Inference Based}} on {{Credible Intervals}} and {{Bayes Factors}}},
  author = {Lovric, Miodrag M.},
  year = {2020},
  month = apr,
  volume = {18},
  pages = {2--27},
  issn = {1538-9472},
  doi = {10.22237/jmasm/1556670540},
  abstract = {In frequentist statistics, point-null hypothesis testing based on significance tests and confidence intervals are harmonious procedures and lead to the same conclusion. This is not the case in the domain of the Bayesian framework. An inference made about the point-null hypothesis using Bayes factor may lead to an opposite conclusion if it is based on the Bayesian credible interval. Bayesian suggestions to test point-nulls using credible intervals are misleading and should be dismissed. A null hypothesized value may be outside a credible interval but supported by Bayes factor (a Type I conflict), or contrariwise, the null value may be inside a credible interval but not supported by the Bayes factor (Type II conflict). Two computer programs in R have been developed that confirm the existence of a countable infinite number of cases, for which Bayes credible intervals are not compatible with Bayesian hypothesis testing.},
  file = {/home/aleksandar/Dropbox/Zotero/Lovric/Lovric_2020_Conflicts in Bayesian Statistics Between Inference Based on Credible Intervals.pdf},
  journal = {Journal of Modern Applied Statistical Methods},
  language = {en},
  number = {1}
}

@article{luAsymptoticResolutionBounds2019,
  title = {Asymptotic Resolution Bounds of Generalized Modularity and Statistically Significant Community Detection},
  author = {Lu, Xiaoyan and Szymanski, Boleslaw K.},
  year = {2019},
  month = feb,
  abstract = {The maximization of generalized modularity performs well on networks in which the members of all communities are statistically indistinguishable from each other. However, there is no theory defining the maximization performance in more realistic networks where edges are heterogeneously distributed within and between communities. We establish the asymptotic theoretical bounds on the resolution parameter of generalized modularity using the random graph properties. From this new perspective on random graph model, we find the resolution limit of modularity maximization can be explained in a surprisingly simple and straightforward way. Given a network produced by the stochastic block models, the communities for which the resolution parameter is larger than their densities are likely to be spread among multiple clusters; while communities for which the resolution parameter is smaller than their background inter-community edge density get merged into one large component. Therefore, no suitable resolution parameter exits when the intra-community edge density in a subgraph is lower than the inter-community edge density in some other subgraph. For such networks, we propose a progressive agglomerative heuristic algorithm to detect statistically significant communities at multiple scales.},
  archivePrefix = {arXiv},
  eprint = {1902.04243},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Zotero/storage/QMGUPCCF/1902.html},
  journal = {arXiv:1902.04243 [physics]},
  primaryClass = {physics}
}

@article{luDiscreteChoiceDataundefined/ed,
  title = {Discrete {{Choice Data}} with {{Unobserved Heterogeneity}}: {{A Conditional Binary Quantile Model}}},
  shorttitle = {Discrete {{Choice Data}} with {{Unobserved Heterogeneity}}},
  author = {Lu, Xiao},
  year = {undefined/ed},
  pages = {1--21},
  issn = {1047-1987, 1476-4989},
  doi = {10.1017/pan.2019.29},
  abstract = {In political science, data with heterogeneous units are used in many studies, such as those involving legislative proposals in different policy areas, electoral choices by different types of voters, and government formation in varying party systems. To disentangle decision-making mechanisms by units, traditional discrete choice models focus exclusively on the conditional mean and ignore the heterogeneous effects within a population. This paper proposes a conditional binary quantile model that goes beyond this limitation to analyze discrete response data with varying alternative-specific features. This model offers an in-depth understanding of the relationship between the explanatory and response variables. Compared to conditional mean-based models, the conditional binary quantile model relies on weak distributional assumptions and is more robust to distributional misspecification. The model also relaxes the assumption of the independence of irrelevant alternatives, which is often violated in practice. The method is applied to a range of political studies to show the heterogeneous effects of explanatory variables across the conditional distribution. Substantive interpretations from counterfactual scenarios are used to illustrate how the conditional binary quantile model captures unobserved heterogeneity, which extant models fail to do. The results point to the risk of averaging out the heterogeneous effects across units by conditional mean-based models.},
  journal = {Political Analysis},
  language = {en}
}

@article{ludkinInferenceGeneralisedStochastic2020,
  title = {Inference for a Generalised Stochastic Block Model with Unknown Number of Blocks and Non-Conjugate Edge Models},
  author = {Ludkin, Matthew},
  year = {2020},
  month = jul,
  pages = {107051},
  issn = {0167-9473},
  doi = {10.1016/j.csda.2020.107051},
  abstract = {The stochastic block model (SBM) is a popular model for capturing community structure and interaction within a network. Network data with non-Boolean edge weights is becoming commonplace; however, existing analysis methods convert such data to a binary representation to apply the SBM, leading to a loss of information. A generalisation of the SBM is considered, which allows edge weights to be modelled in their recorded state. An effective reversible jump Markov chain Monte Carlo sampler is proposed for estimating the parameters and the number of blocks for this generalised SBM. The methodology permits non-conjugate distributions for edge weights, which enable more flexible modelling than current methods as illustrated on synthetic data, a network of brain activity and an email communication network.},
  file = {/home/aleksandar/Zotero/storage/PTHTMT33/S0167947320301420.html},
  journal = {Computational Statistics \& Data Analysis},
  language = {en}
}

@article{luEvolutionModelGroup,
  title = {An {{Evolution Model}} of {{Group Opinions Based}} on {{Social Judgment Theory}} ({{August}} 2018)},
  author = {Lu, An and Ding, Zhengping and Liu, Yezheng and Guo, Yaguang and Jiang, Yuanchun and Chen, Zhengguang},
  doi = {8492459},
  abstract = {We integrate social judgment theory and bounded confidence to build an agent-based model for exploring how group opinions form and evolve. Each agent in our model, which is an extended Hegselmann\textendash Krause model, has its own personality (openness and vacillation) and emotion (opinion). In a homogeneous case, we find that agents' opinions will preserve their initial order. Once a crack forms between two agents at time {$<$}inline-formula{$>$} {$<$}tex-math notation="LaTeX"{$>\$$}t\$ {$<$}/tex-math{$><$}/inline-formula{$>$}, then a crack will exist after that time. Despite the fact that the continuous trust function results in a zero infimum of weight, the opinion evolution system will converge. Simulation shows that an increased level of either openness or vacillation can cause the group to reach a consensus easily. Specifically, the higher the level of openness of an agent, the greater the amount of clusters; and the higher the level of vacillation of an agent, the less time it will take for group opinions to converge. Although the initial opinions are randomly distributed, agents with the same levels of vacillation and openness tend to be concentrated during the evolution process.},
  journal = {IEEE Access}
}

@article{luEvolutionModelGroupa,
  title = {An {{Evolution Model}} of {{Group Opinions Based}} on {{Social Judgment Theory}} ({{August}} 2018)},
  author = {Lu, An and Ding, Zhengping and Liu, Yezheng and Guo, Yaguang and Jiang, Yuanchun and Chen, Zhengguang},
  doi = {8492459},
  abstract = {We integrate social judgment theory and bounded confidence to build an agent-based model for exploring how group opinions form and evolve. Each agent in our model, which is an extended Hegselmann\textendash Krause model, has its own personality (openness and vacillation) and emotion (opinion). In a homogeneous case, we find that agents' opinions will preserve their initial order. Once a crack forms between two agents at time {$<$}inline-formula{$>$} {$<$}tex-math notation="LaTeX"{$>\$$}t\$ {$<$}/tex-math{$><$}/inline-formula{$>$}, then a crack will exist after that time. Despite the fact that the continuous trust function results in a zero infimum of weight, the opinion evolution system will converge. Simulation shows that an increased level of either openness or vacillation can cause the group to reach a consensus easily. Specifically, the higher the level of openness of an agent, the greater the amount of clusters; and the higher the level of vacillation of an agent, the less time it will take for group opinions to converge. Although the initial opinions are randomly distributed, agents with the same levels of vacillation and openness tend to be concentrated during the evolution process.},
  journal = {IEEE Access}
}

@article{luftTheorizingMoralCognition2020,
  title = {Theorizing {{Moral Cognition}}: {{Culture}} in {{Action}}, {{Situations}}, and {{Relationships}}},
  shorttitle = {Theorizing {{Moral Cognition}}},
  author = {Luft, Aliza},
  year = {2020},
  month = jan,
  volume = {6},
  pages = {2378023120916125},
  publisher = {{SAGE Publications}},
  issn = {2378-0231},
  doi = {10.1177/2378023120916125},
  abstract = {Dual-process theories of morality are approaches to moral cognition that stress the varying significance of emotion and deliberation in shaping judgments of action. Sociological research that builds on these ideas considers how cross-cultural variation alters judgments, with important consequences for what is and is not considered moral behavior. Yet lacking from these approaches is the notion that, depending on the situation and relationship, the same behavior by the same person can be considered more or less moral. The author reviews recent trends in sociological theorizing about morality and calls attention to the neglect of situational variations and social perceptions as mediating influences on judgment. She then analyzes the moral machine experiment to demonstrate how situations and relationships inform moral cognition. Finally, the author suggests that we can extend contemporary trends in the sociology of morality by connecting culture in thinking about action to culture in thinking about people.},
  journal = {Socius}
}

@article{luhrmannThirdWaveAutocratization2019,
  title = {A Third Wave of Autocratization Is Here: What Is New about It?},
  shorttitle = {A Third Wave of Autocratization Is Here},
  author = {L{\"u}hrmann, Anna and Lindberg, Staffan I.},
  year = {2019},
  month = oct,
  volume = {26},
  pages = {1095--1113},
  issn = {1351-0347, 1743-890X},
  doi = {10.1080/13510347.2019.1582029},
  journal = {Democratization},
  language = {en},
  number = {7}
}

@article{lukicImmigrantIntegrationRegimes2020,
  title = {Immigrant Integration Regimes in {{Europe}}: {{Incorporating}} the {{Western Balkan}} Countries},
  shorttitle = {Immigrant Integration Regimes in {{Europe}}},
  author = {Luki{\'c}, Vesna and Toma{\v s}evi{\'c}, Aleksandar},
  year = {2020},
  month = may,
  volume = {60},
  pages = {143--153-143--153},
  issn = {1581-8314},
  doi = {10.3986/AGS.7286},
  abstract = {This article discusses immigrant integration policies in Europe. We analyzed data from the 2015 Migration Integration Policy Index to identify similar immigrant integration regimes in Europe according to policy priorities related to immigrants' socioeconomic rights. The results of a latent class analysis demonstrated that there are two immigrant integration policy regimes among the EU 28, Albania, Bosnia and Herzegovina, Montenegro, Macedonia, and Serbia, with variation between the old EU member states since 1995 (without Greece) and Estonia versus the new EU member states since 2004 (without Estonia), Greece, and the Western Balkan countries. Based on our classification, we conclude that there is a trend of convergence in integration policy regimes in Europe, in which the effects of spatial/geographical and temporal dimensions are manifested.},
  copyright = {Copyright (c) 2020 ZRC SAZU Anton Melik Geographical Institute},
  file = {/home/aleksandar/Zotero/storage/3RAYTEJM/Lukić_Tomašević_2020_Immigrant integration regimes in Europe.pdf;/home/aleksandar/Zotero/storage/8H6JJBEW/7286.html;/home/aleksandar/Zotero/storage/FNS6SEJA/7286.html},
  journal = {Acta geographica Slovenica},
  keywords = {Europe,immigration,integration policy,latent class analysis,Migrant Integration Policy Index},
  language = {en},
  number = {1}
}

@article{luoNetworkOpinionEvolution2020,
  title = {Network {{Opinion Evolution Model Incorporating}} the {{Influence}} of {{Media Heterogeneity}}},
  author = {Luo, G. and Zhang, Z. and Zhang, Z. and Mi, K.},
  year = {2020},
  volume = {8},
  pages = {172498--172508},
  issn = {2169-3536},
  doi = {10.1109/ACCESS.2020.3025400},
  abstract = {Opinion dynamics is the basic research content of social network research. Traditional opinion dynamics models focus on the heterogeneity of regular users, but ignore the heterogeneity of media users in social networks. Compared to regular users, media users have the characteristics of releasing more information and having greater influence, and their opinions are not easily affected by regular users. Aiming at the characteristics of information transmission with the participation of media users, we propose an opinion dynamics model to reproduce a realistic process of opinion interaction between regular users and media users and between regular users, respectively. The time variable is also incorporated into the probability of opinion adoption to make sure the opinion interaction model has timeliness. The simulation experiments on models of four typical complex network structures have shown that the characteristic of large average path length in a regular network will lead to the media opinion spreading slowly with a small scope, and a low individual opinion interaction frequency. On the other hand, the characteristics of strong heterogeneity and many center users in a scale-free network lead to media opinion spreading fast with a big scope. Moreover, the opinion adoption probability has a great influence on the average opinion value of the final state in four networks, while the bounded trust threshold has a great influence on opinion evolution in regular networks and scale-free networks. Furthermore, the media location selection approach based on the node betweenness results in opinion influence transmission with high speed and large range compared with the approaches based on node degree and clustering coefficient.},
  file = {/home/aleksandar/Documents/Zotero/Luo et al/Luo et al_2020_Network Opinion Evolution Model Incorporating the Influence of Media.pdf;/home/aleksandar/Zotero/storage/H2L3JM7F/9201277.html},
  journal = {IEEE Access}
}

@article{lutovBridgingGapCommunity,
  title = {Bridging the {{Gap}} between {{Community}} and {{Node Representations}}: {{Graph Embedding}} via {{Community Detection}}},
  author = {Lutov, Artem and Yang, Dingqi and {Cudre-Mauroux}, Philippe},
  pages = {10},
  abstract = {Graph embedding has become a key component of many data mining and analysis systems. Current graph embedding approaches either sample a large number of node pairs from a graph to learn node embeddings via stochastic optimization or factorize a high-order node proximity/adjacency matrix via computationally intensive matrix factorization techniques. These approaches typically require significant resources for the learning process and rely on multiple parameters, which limits their applicability in practice. Moreover, most of the existing graph embedding techniques operate effectively in one specific metric space only (e.g., the one produced with cosine similarity), do not preserve higher-order structural features of the input graph and cannot automatically determine a meaningful number of dimensions for the embedding space. Typically, the produced embeddings are not easily interpretable, which complicates further analyses and limits their applicability. To address these issues, we propose DAOR, a highly efficient and parameter-free graph embedding technique producing metric space-robust, compact and interpretable embeddings without any manual tuning. Compared to a dozen state-of-the-art graph embedding algorithms, DAOR yields competitive results on both node classification (which benefits form high-order proximity) and link prediction (which relies on low-order proximity mostly). Unlike existing techniques, however, DAOR does not require any parameter tuning and improves the embeddings generation speed by several orders of magnitude. Our approach has hence the ambition to greatly simplify and speed up data analysis tasks involving graph representation learning.},
  file = {/home/aleksandar/Dropbox/Zotero/Lutov et al/Lutov et al_Bridging the Gap between Community and Node Representations.pdf},
  language = {en}
}

@article{lvNovelMeasureIdentifying,
  title = {A Novel Measure of Identifying Influential Nodes in Complex Networks},
  author = {Lv, Zhiwei and Zhao, Nan and Xiong, Fei and Chen, Nan},
  doi = {10.1016/j.physa.2019.01.136},
  abstract = {{$<$}div{$><$}h2{$>$}Abstract{$<$}/h2{$><$}p{$>$}Research about ranking nodes according to their spreading ability in complex networks is a fundamental and essential issue. As one of the vital centrality measures, the degree centrality is very simple. However, it is difficult to distinguish the importance of nodes with the same degree. Global metrics such as betweenness centrality and closeness centrality can identify influential nodes more accurately, but there remains some disadvantages and limitations. In this paper, we propose an average shortest path centrality to rank the spreaders, in which the relative change of the average shortest path of the whole network is taken into account. For evaluating the performance, we adapt Susceptible-Infected-Recovered model to simulate the epidemic spreading process on four different real networks. The experimental and simulated results show that our scheme owns better performance compared with degree, betweenness and closeness centrality.{$<$}/p{$><$}/div{$>$}},
  journal = {Physica A: Statistical Mechanics and its Applications}
}

@article{lvNovelMeasureIdentifyinga,
  title = {A Novel Measure of Identifying Influential Nodes in Complex Networks},
  author = {Lv, Zhiwei and Zhao, Nan and Xiong, Fei and Chen, Nan},
  doi = {S0378437119301438},
  abstract = {{$<$}p{$>$}Publication date: Available online 1 February 2019{$<$}/p{$><$}p{$>$}\textbf{Source:} Physica A: Statistical Mechanics and its Applications{$<$}/p{$><$}p{$>$}Author(s): Zhiwei Lv, Nan Zhao, Fei Xiong, Nan Chen{$<$}/p{$><$}h5{$>$}Abstract{$<$}/h5{$><$}div{$><$}p{$>$}Research about ranking nodes according to their spreading ability in complex networks is a fundamental and essential issue. As one of the vital centrality measures, the degree centrality is very simple. However, it is difficult to distinguish the importance of nodes with the same degree. Global metrics such as betweenness centrality and closeness centrality can identify influential nodes more accurately, but there remains some disadvantages and limitations. In this paper, we propose an average shortest path centrality to rank the spreaders, in which the relative change of the average shortest path of the whole network is taken into account. For evaluating the performance, we adapt Susceptible-Infected-Recovered model to simulate the epidemic spreading process on four different real networks. The experimental and simulated results show that our scheme owns better performance compared with degree, betweenness and closeness centrality.{$<$}/p{$><$}/div{$>$}},
  journal = {Physica A: Statistical Mechanics and its Applications}
}

@article{lvNovelMeasureIdentifyingb,
  title = {A Novel Measure of Identifying Influential Nodes in Complex Networks},
  author = {Lv, Zhiwei and Zhao, Nan and Xiong, Fei and Chen, Nan},
  doi = {10.1016/j.physa.2019.01.136},
  abstract = {{$<$}div{$><$}h2{$>$}Abstract{$<$}/h2{$><$}p{$>$}Research about ranking nodes according to their spreading ability in complex networks is a fundamental and essential issue. As one of the vital centrality measures, the degree centrality is very simple. However, it is difficult to distinguish the importance of nodes with the same degree. Global metrics such as betweenness centrality and closeness centrality can identify influential nodes more accurately, but there remains some disadvantages and limitations. In this paper, we propose an average shortest path centrality to rank the spreaders, in which the relative change of the average shortest path of the whole network is taken into account. For evaluating the performance, we adapt Susceptible-Infected-Recovered model to simulate the epidemic spreading process on four different real networks. The experimental and simulated results show that our scheme owns better performance compared with degree, betweenness and closeness centrality.{$<$}/p{$><$}/div{$>$}},
  journal = {Physica A: Statistical Mechanics and its Applications}
}

@article{lvNovelMeasureIdentifyingc,
  title = {A Novel Measure of Identifying Influential Nodes in Complex Networks},
  author = {Lv, Zhiwei and Zhao, Nan and Xiong, Fei and Chen, Nan},
  doi = {S0378437119301438},
  abstract = {{$<$}p{$>$}Publication date: Available online 1 February 2019{$<$}/p{$><$}p{$>$}\textbf{Source:} Physica A: Statistical Mechanics and its Applications{$<$}/p{$><$}p{$>$}Author(s): Zhiwei Lv, Nan Zhao, Fei Xiong, Nan Chen{$<$}/p{$><$}h5{$>$}Abstract{$<$}/h5{$><$}div{$><$}p{$>$}Research about ranking nodes according to their spreading ability in complex networks is a fundamental and essential issue. As one of the vital centrality measures, the degree centrality is very simple. However, it is difficult to distinguish the importance of nodes with the same degree. Global metrics such as betweenness centrality and closeness centrality can identify influential nodes more accurately, but there remains some disadvantages and limitations. In this paper, we propose an average shortest path centrality to rank the spreaders, in which the relative change of the average shortest path of the whole network is taken into account. For evaluating the performance, we adapt Susceptible-Infected-Recovered model to simulate the epidemic spreading process on four different real networks. The experimental and simulated results show that our scheme owns better performance compared with degree, betweenness and closeness centrality.{$<$}/p{$><$}/div{$>$}},
  journal = {Physica A: Statistical Mechanics and its Applications}
}

@article{lynamPerilsPartiallingCautionary2006,
  title = {The {{Perils}} of {{Partialling}}: {{Cautionary Tales}} from {{Aggression}} and {{Psychopathy}}},
  shorttitle = {The {{Perils}} of {{Partialling}}},
  author = {Lynam, Donald R. and Hoyle, Rick H. and Newman, Joseph P.},
  year = {2006},
  month = sep,
  volume = {13},
  pages = {328--341},
  issn = {1073-1911, 1552-3489},
  doi = {10.1177/1073191106290562},
  journal = {Assessment},
  language = {en},
  number = {3}
}

@article{lynnStatisticalMechanicsHuman,
  title = {The Statistical Mechanics of Human Behavior},
  author = {Lynn, Christopher W},
  pages = {24},
  file = {/home/aleksandar/Zotero/storage/IYLEJMXS/Lynn - The statistical mechanics of human behavior.pdf},
  language = {en}
}

@article{lynnStructureNoiseMental,
  title = {Structure from Noise: {{Mental}} Errors Yield Abstract Representations of Events.},
  author = {Lynn, Christopher W. and Kahn, Ari E. and Bassett, Danielle S.},
  doi = {arXiv:1805.12491v2},
  abstract = {{$<$}p{$>$}Humans are adept at uncovering abstract associations in the world around them, yet the underlying mechanisms remain poorly understood. Intuitively, learning the higher-order structure of statistical relationships should involve complex mental processes. Here we propose an alternative perspective: that higher-order associations instead arise from natural errors in learning and memory. Combining ideas from information theory and reinforcement learning, we derive a maximum entropy (or minimum complexity) model of people's internal representations of the transitions between stimuli. Importantly, our model (i) affords a concise analytic form, (ii) qualitatively explains the effects of transition network structure on human expectations, and (iii) quantitatively predicts human reaction times in probabilistic sequential motor tasks. Together, these results suggest that mental errors influence our abstract representations of the world in significant and predictable ways, with direct implications for the study and design of optimally learnable information sources. {$<$}/p{$>$}},
  journal = {arXiv Physics and Society}
}

@article{lynnStructureNoiseMentala,
  title = {Structure from Noise: {{Mental}} Errors Yield Abstract Representations of Events.},
  author = {Lynn, Christopher W. and Kahn, Ari E. and Bassett, Danielle S.},
  doi = {arXiv:1805.12491v2},
  abstract = {{$<$}p{$>$}Humans are adept at uncovering abstract associations in the world around them, yet the underlying mechanisms remain poorly understood. Intuitively, learning the higher-order structure of statistical relationships should involve complex mental processes. Here we propose an alternative perspective: that higher-order associations instead arise from natural errors in learning and memory. Combining ideas from information theory and reinforcement learning, we derive a maximum entropy (or minimum complexity) model of people's internal representations of the transitions between stimuli. Importantly, our model (i) affords a concise analytic form, (ii) qualitatively explains the effects of transition network structure on human expectations, and (iii) quantitatively predicts human reaction times in probabilistic sequential motor tasks. Together, these results suggest that mental errors influence our abstract representations of the world in significant and predictable ways, with direct implications for the study and design of optimally learnable information sources. {$<$}/p{$>$}},
  journal = {arXiv Physics and Society}
}

@article{lynnSurgesCollectiveHuman,
  title = {Surges of Collective Human Activity Emerge from Simple Pairwise Correlations.},
  author = {Lynn, Christopher W. and Papadopoulos, Lia and Lee, Daniel D. and Bassett, Danielle S.},
  doi = {arXiv:1803.00118v4},
  abstract = {{$<$}p{$>$}Human populations exhibit complex behaviors---characterized by long-range correlations and surges in activity---across a range of social, political, and technological contexts. Yet it remains unclear where these collective behaviors come from, or if there even exists a set of unifying principles. Indeed, existing explanations typically rely on context-specific mechanisms, such as traffic jams driven by work schedules or spikes in online traffic induced by significant events. However, analogies with statistical mechanics suggest a more general mechanism: that collective patterns can emerge organically from fine-scale interactions within a population. Here, across four different modes of human activity, we show that the simplest correlations in a population---those between pairs of individuals---can yield accurate quantitative predictions for the large-scale behavior of the entire population. To quantify the minimal consequences of pairwise correlations, we employ the principle of maximum entropy, making our description equivalent to an Ising model whose interactions and external fields are notably calculated from past observations of population activity. In addition to providing accurate quantitative predictions, we show that the topology of learned Ising interactions resembles the network of inter-human communication within a population. Together, these results demonstrate that fine-scale correlations can be used to predict large-scale social behaviors, a perspective that has critical implications for modeling and resource allocation in human populations. {$<$}/p{$>$}},
  journal = {arXiv Social and Information Networks}
}

@article{lynnSurgesCollectiveHumana,
  title = {Surges of Collective Human Activity Emerge from Simple Pairwise Correlations.},
  author = {Lynn, Christopher W. and Papadopoulos, Lia and Lee, Daniel D. and Bassett, Danielle S.},
  doi = {arXiv:1803.00118v4},
  abstract = {{$<$}p{$>$}Human populations exhibit complex behaviors---characterized by long-range correlations and surges in activity---across a range of social, political, and technological contexts. Yet it remains unclear where these collective behaviors come from, or if there even exists a set of unifying principles. Indeed, existing explanations typically rely on context-specific mechanisms, such as traffic jams driven by work schedules or spikes in online traffic induced by significant events. However, analogies with statistical mechanics suggest a more general mechanism: that collective patterns can emerge organically from fine-scale interactions within a population. Here, across four different modes of human activity, we show that the simplest correlations in a population---those between pairs of individuals---can yield accurate quantitative predictions for the large-scale behavior of the entire population. To quantify the minimal consequences of pairwise correlations, we employ the principle of maximum entropy, making our description equivalent to an Ising model whose interactions and external fields are notably calculated from past observations of population activity. In addition to providing accurate quantitative predictions, we show that the topology of learned Ising interactions resembles the network of inter-human communication within a population. Together, these results demonstrate that fine-scale correlations can be used to predict large-scale social behaviors, a perspective that has critical implications for modeling and resource allocation in human populations. {$<$}/p{$>$}},
  journal = {arXiv Social and Information Networks}
}

@article{macdonaldClassAttitudesPolitical,
  title = {Class {{Attitudes}}, {{Political Knowledge}}, and {{Support}} for {{Redistribution}} in an {{Era}} of {{Inequality}}},
  author = {Macdonald, David},
  volume = {n/a},
  issn = {1540-6237},
  doi = {10.1111/ssqu.12767},
  abstract = {Objective Why, despite positive feelings toward the poor and working classes, relative to the rich and big business, has American public support for redistribution failed to appreciably increase during an era of high, and rising, income inequality? Methods I argue that this puzzling disconnect is due, in part, to a lack of general political knowledge. Using survey data from the 2012 American National Election Study, I test how political knowledge conditions the relationship between people's economic class group attitudes and their support for redistribution. Results People with low (high) levels of political knowledge weakly (strongly) connect their class attitudes with support for redistributive spending and progressive taxation. Data from four ANES panel studies show that this does not result from the less knowledgeable holding weak ``nonattitudes'' toward these class groups. Rather, consistent with Converse's classic work, I attribute this to less knowledgeable individuals lacking awareness about how redistributive policies benefit different social groups. Conclusion These findings help us to better understand an important puzzle in American politics: why a mass public that purports to favor the poor and working classes over the economic elite has not turned more strongly in favor of redistribution during an era of historic inequality.},
  copyright = {\textcopyright{} 2020 by the Southwestern Social Science Association},
  file = {/home/aleksandar/Zotero/storage/PJMS2D8X/ssqu.html},
  journal = {Social Science Quarterly},
  language = {en},
  number = {n/a}
}

@article{maceachernTwodatasetProblem2019,
  title = {On the Two-Dataset Problem},
  author = {MacEachern, Steven N. and Miyawaki, Koji},
  year = {2019},
  month = nov,
  abstract = {This paper considers the two-dataset problem, where data are collected from two potentially different populations sharing common aspects. This problem arises when data are collected by two different types of researchers or from two different sources. We may reach invalid conclusions if knowledge about the data collection process is ignored or assumptions about the process are wrong. To address this problem, this paper develops statistical models and proposes two prediction errors that can be used to evaluate the underlying data collection process. As a consequence, it is possible to discuss the heterogeneity/similarity of data in terms of prediction. Two real datasets are selected to illustrate our method.},
  archivePrefix = {arXiv},
  eprint = {1911.00204},
  eprinttype = {arxiv},
  journal = {arXiv:1911.00204 [stat]},
  language = {en},
  primaryClass = {stat}
}

@article{maciejovskyTooMuchTrust2020,
  title = {Too {{Much Trust}} in {{Group Decisions}}: {{Uncovering Hidden Profiles}} by {{Groups}} and {{Markets}}},
  shorttitle = {Too {{Much Trust}} in {{Group Decisions}}},
  author = {Maciejovsky, Boris and Budescu, David V.},
  year = {2020},
  month = aug,
  publisher = {{INFORMS}},
  issn = {1047-7039},
  doi = {10.1287/orsc.2020.1363},
  abstract = {A crucial challenge for organizations is to pool and aggregate information effectively. Traditionally, organizations have relied on committees and teams, but recently many organizations have explored the use of information markets. In this paper, the authors compared groups and markets in their ability to pool and aggregate information in a hidden-profiles task. In Study 1, groups outperformed markets when there were no conflicts of interest among participants, whereas markets outperformed groups when conflicts of interest were present. Also, participants had more trust in groups to uncover hidden profiles than in markets. Study 2 generalized these findings to a simple prediction task, confirming that people had more trust in groups than in markets. These results were not qualified by conflicts of interest. Drawing on experienced forecasters from Good Judgment Open, Study 3 found that familiarity and experience with markets increased the endorsement and use of markets relative to traditional committees.},
  file = {/home/aleksandar/Zotero/storage/SBZDCICA/orsc.2020.html},
  journal = {Organization Science}
}

@article{maerzStateWorld20192020,
  title = {State of the World 2019: Autocratization Surges \textendash{} Resistance Grows},
  shorttitle = {State of the World 2019},
  author = {Maerz, Seraphine F. and L{\"u}hrmann, Anna and Hellmeier, Sebastian and Grahn, Sandra and Lindberg, Staffan I.},
  year = {2020},
  month = may,
  volume = {0},
  pages = {1--19},
  publisher = {{Routledge}},
  issn = {1351-0347},
  doi = {10.1080/13510347.2020.1758670},
  abstract = {This article analyses the state of democracy in the world in 2019. We demonstrate that the ``third wave of autocratization'' is accelerating and deepening. The dramatic loss of eight democracies in the last year sets a new record in the rate of breakdowns. Exemplifying this crisis is Hungary, now the EU's first ever authoritarian member state. Governmental assaults on civil society, freedom of expression, and the media are proliferating and becoming more severe. A new and disturbing trend is that the quality of elections is now also deteriorating in many countries. Nevertheless, there are also positive signs: pro-democracy protests reached an all-time high in 2019. People are taking to the streets to protest the erosion of democracies and challenge dictators. Popular protests have contributed to substantial democratization in 22 countries over the last ten years \textendash{} including Armenia, Tunisia, and Ecuador. This was before the Covid-19 pandemic. Responses to the crisis, including many states of emergencies, risk further accelerating autocratization.},
  annotation = {\_eprint: https://doi.org/10.1080/13510347.2020.1758670},
  journal = {Democratization},
  number = {0}
}

@article{magdon-ismailIntrinsicScaleNetworks,
  title = {The {{Intrinsic Scale}} of {{Networks}} Is {{Small}}.},
  author = {{Magdon-ismail}, Malik and Hegde, Kshiteesh},
  doi = {arXiv:1901.09680v1},
  abstract = {{$<$}p{$>$}We define the intrinsic scale at which a network begins to reveal its identity as the scale at which subgraphs in the network (created by a random walk) are distinguishable from similar sized subgraphs in a perturbed copy of the network. We conduct an extensive study of intrinsic scale for several networks, ranging from structured (e.g. road networks) to ad-hoc and unstructured (e.g. crowd sourced information networks), to biological. We find: (a) The intrinsic scale is surprisingly small (7-20 vertices), even though the networks are many orders of magnitude larger. (b) The intrinsic scale quantifies ``structure'' in a network -- networks which are explicitly constructed for specific tasks have smaller intrinsic scale. (c) The structure at different scales can be fragile (easy to disrupt) or robust. {$<$}/p{$>$}},
  journal = {arXiv Physics and Society}
}

@article{magdon-ismailIntrinsicScaleNetworksa,
  title = {The {{Intrinsic Scale}} of {{Networks}} Is {{Small}}.},
  author = {{Magdon-ismail}, Malik and Hegde, Kshiteesh},
  doi = {arXiv:1901.09680v1},
  abstract = {{$<$}p{$>$}We define the intrinsic scale at which a network begins to reveal its identity as the scale at which subgraphs in the network (created by a random walk) are distinguishable from similar sized subgraphs in a perturbed copy of the network. We conduct an extensive study of intrinsic scale for several networks, ranging from structured (e.g. road networks) to ad-hoc and unstructured (e.g. crowd sourced information networks), to biological. We find: (a) The intrinsic scale is surprisingly small (7-20 vertices), even though the networks are many orders of magnitude larger. (b) The intrinsic scale quantifies ``structure'' in a network -- networks which are explicitly constructed for specific tasks have smaller intrinsic scale. (c) The structure at different scales can be fragile (easy to disrupt) or robust. {$<$}/p{$>$}},
  journal = {arXiv Physics and Society}
}

@article{maghenemLyapunovbasedSynchronizationNetworked2020,
  title = {Lyapunov-Based Synchronization of Networked Systems: {{From}} Continuous-Time to Hybrid Dynamics},
  shorttitle = {Lyapunov-Based Synchronization of Networked Systems},
  author = {Maghenem, Mohamed and Postoyan, Romain and Lor{\'i}a, Antonio and Panteley, Elena},
  year = {2020},
  month = jul,
  issn = {1367-5788},
  doi = {10.1016/j.arcontrol.2020.06.003},
  abstract = {Synchronization pertains to the property of interconnected systems according to which their dynamic behavior is coordinated in an appropriate sense. That is to say, some of their state variables, or functions of the latter for that matter, converge to each other. Synchronization may occur naturally or may be induced, controlled, and it may be present between two systems or among a large number. In the latter case, it is convenient to speak of a network of interconnected systems. Understanding synchronization, and how to control it, is an important paradigm as it is present in a variety of scenarios. These involve, e.g., networks of technological systems (robots and vehicles of different kinds), social networks (by which people exchange opinions and agree, or not), networks of biological systems (uni or pluricellular), etc. Owing to the context, the mathematical models to describe such networks and to define synchronization formally, varies dramatically. Ordinary continuous-time or discrete-time models for which modern control theory and Lyapunov stability theory are tailored result inappropriate to incorporate hybrid phenomena that intervene in the network. These may stem from sudden topology changes, the use of digital or intermittent control strategies, the presence of impacts in the intrinsic dynamics of the nodes, etc. In this invited paper, we give an overview of synchronization control problems, mostly of cooperative control of networks of autonomous vehicles (based on continuous-time models). For that matter, the first part of the paper focuses on the main contributions of [1]. Then, we give further perspectives on what we consider significant open problems on synchronization of hybrid systems and hybrid networks.},
  file = {/home/aleksandar/Zotero/storage/KCN5CF5J/S1367578820300523.html},
  journal = {Annual Reviews in Control},
  language = {en}
}

@article{mahabadiSLPAbasedParallelOverlapping2020,
  title = {{{SLPA}}-Based Parallel Overlapping Community Detection Approach in Large Complex Social Networks},
  author = {Mahabadi, Aminollah and Hosseini, Mohammad},
  year = {2020},
  month = oct,
  issn = {1573-7721},
  doi = {10.1007/s11042-020-09993-1},
  abstract = {Performance improvement of community detection is an NP problem in large social networks analysis where by integrating the overlapped communities' information and modularity maximization increases the time complexity and memory usage. This paper presents an online parallel overlapping community detection approach based on a speaker-listener propagation algorithm by proposing a novel parallel algorithm and applying three new metrics. This approach is presented to improve modularity and expand scalability for getting a significantly speedup in low time-consuming and usage memory through an agent-based parallel implementation in a multi-core architecture. The key ideas of our approach are increasing the communities' conductance score, limiting the speaking-listening stages and executing a strategic updating order to develop a speaker-listeners label propagation algorithm for getting better speedup and semi-deterministic results without using prior training or requiring particular predefined features. Experimental results of used large datasets compared with state-of-the-art algorithms show that the proposed method is extremely convergence and achieves an average 820\% speedup in the label propagation algorithm, and significantly improves the modularity that are effective in finding better overlapping communities in a linear time complexity O(m) and lower usage memory O(n).},
  journal = {Multimedia Tools and Applications},
  language = {en}
}

@article{maherMappingPublicHealth2020,
  title = {Mapping Public Health Responses with Attitude Networks: The Emergence of Opinion-Based Groups in the {{UK}}'s Early {{COVID}}-19 Response Phase},
  shorttitle = {Mapping Public Health Responses with Attitude Networks},
  author = {Maher, Paul J. and MacCarron, P{\'a}draig and Quayle, Michael},
  year = {2020},
  volume = {59},
  pages = {641--652},
  issn = {2044-8309},
  doi = {10.1111/bjso.12396},
  abstract = {Partisan patterns of compliance with public health measures are a feature of early COVID-19 responses. In many cases, these differences in behaviour relate to pre-existing group identities. However, in times of rapid societal change, novel opinion-based groups can emerge and provide a new basis for partisan identification and divergent collective behaviour. Here, we use network methods to map the emergence of opposing opinion-based groups and assess their implications for public health behaviour. In a longitudinal study, we tracked public health attitudes and self-reported behaviour in a sample of UK participants over four time points. Network visualisation reveal a rift in attitudinal alignment over time and the genesis of two distinct groups characterised by trust, or distrust, in science (Study 1a; N = 253). These groups also diverge in public health behaviour. In a brief follow-up study (N = 206), we find that this opinion polarization partially reflects underlying societal divides. We discuss implications for opinion-based group research and public health campaigns.},
  annotation = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/bjso.12396},
  copyright = {\textcopyright{} 2020 The Authors. British Journal of Social Psychology published by John Wiley \& Sons Ltd on behalf of British Psychological Society},
  file = {/home/aleksandar/Zotero/storage/KX9D5RPI/Maher et al. - 2020 - Mapping public health responses with attitude netw.pdf;/home/aleksandar/Zotero/storage/DG52CRJL/bjso.html},
  journal = {British Journal of Social Psychology},
  language = {en},
  number = {3}
}

@inproceedings{maheswaranGravityBasedTrustModel2007,
  title = {Towards a {{Gravity}}-{{Based Trust Model}} for {{Social Networking Systems}}},
  booktitle = {27th {{International Conference}} on {{Distributed Computing Systems Workshops}} ({{ICDCSW}}'07)},
  author = {Maheswaran, Muthucumaru and Tang, Hon Cheong and Ghunaim, Ahmad},
  year = {2007},
  month = jun,
  pages = {24--24},
  issn = {1545-0678},
  doi = {10.1109/ICDCSW.2007.82},
  abstract = {Web-based social networks are emerging as the top applications on the Internet. With this immense popularity, many of the shortcomings of the current social network deployments are also coming to light. One of the glaring problems with existing web-based social networks is trust management. In this paper, we focus on trust modeling in social networks. Another allied issue that is not considered here is using trust in managing the activities within the social network. We introduce a gravity-based model for estimating trust. We present the complete model along with the trust computation algorithms. We present initial results from a simulation study that investigates the feasibility of the proposed scheme.},
  file = {/home/aleksandar/Dropbox/Zotero/Maheswaran et al/Maheswaran et al_2007_Towards a Gravity-Based Trust Model for Social Networking Systems.pdf;/home/aleksandar/Zotero/storage/3WHKI79B/4279020.html},
  keywords = {DONE}
}

@article{maiaAdaptiveNetworkApproach2020,
  title = {Adaptive Network Approach for Emergence of Societal Bubbles},
  author = {Maia, Hugo P. and Ferreira, Silvio C. and Martins, Marcelo L.},
  year = {2020},
  month = oct,
  abstract = {Far beyond its relevance for commercial and political marketings, opinion formation and decision making processes are central for representative democracy, government functioning, and state organization. In the present report, a stochastic agent-based model is investigated. The model assumes that bounded confidence and homophily mechanisms drive both opinion dynamics and social network evolution through either rewiring or breakage of social contacts. In addition to the classical transition from global consensus to opinion polarization, our main findings are (i) a cascade of fragmentation of the social network into echo chambers (modules) holding distinct opinions and rupture of the bridges interconnecting these modules as the tolerance for opinion differences increases. There are multiple surviving opinions associated to these modules within which consensus is formed; and (ii) the adaptive social network exhibits a hysteresis-like behavior characterized by irreversible changes in its topology as the opinion tolerance cycles from radicalization towards consensus and backward to radicalization.},
  archivePrefix = {arXiv},
  eprint = {2010.08635},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Maia et al/Maia et al_2020_Adaptive network approach for emergence of societal bubbles.pdf;/home/aleksandar/Zotero/storage/7W4CQ9S7/2010.html},
  journal = {arXiv:2010.08635 [nlin, physics:physics]},
  primaryClass = {nlin, physics:physics}
}

@inproceedings{mainwaringExtraSystemVolatility2009,
  title = {Extra System Volatility and the Vote Share of Young Parties},
  booktitle = {Annual {{Meeting}} of the {{Canadian Political Science Association}}},
  author = {Mainwaring, Scott and {Espa{\~n}a-Najera}, Annabella and Gervasoni, Carlos},
  year = {2009},
  address = {{Ottawa, Canada}}
}

@article{mainwaringExtraWithinsystemElectoral2017,
  title = {Extra- and within-System Electoral Volatility},
  author = {Mainwaring, Scott and Gervasoni, Carlos and {Espa{\~n}a-Najera}, Annabella},
  year = {2017},
  month = nov,
  volume = {23},
  pages = {623--635},
  issn = {1354-0688, 1460-3683},
  doi = {10.1177/1354068815625229},
  journal = {Party Politics},
  language = {en},
  number = {6}
}

@article{mainwaringPoliticalSequencesStabilization2007,
  title = {Political Sequences and the Stabilization of Interparty Competition: {{Electoral}} Volatility in Old and New Democracies},
  shorttitle = {Political {{Sequences}} and the {{Stabilization}} of {{Interparty Competition}}},
  author = {Mainwaring, Scott and Zoco, Edurne},
  year = {2007},
  month = mar,
  volume = {13},
  pages = {155--178},
  issn = {1354-0688, 1460-3683},
  doi = {10.1177/1354068807073852},
  journal = {Party Politics},
  language = {en},
  number = {2}
}

@article{mairDemocracyParties2005,
  title = {Democracy beyond Parties},
  author = {Mair, Peter},
  year = {2005},
  journal = {SPS Working Papers},
  series = {05-06}
}

@incollection{mairPartySystemsAlternation2007,
  title = {Party Systems and Alternation in Government, 1950-2000. {{Innovation}} and Institutionalisation},
  booktitle = {Globalisation and Democratisation. {{Challenges}} for Political Parties},
  author = {Mair, Peter},
  editor = {Gloppen, Siri and Rakner, Lise},
  year = {2007},
  pages = {135--154},
  publisher = {{Fagbokforlaget}},
  address = {{Bergen}}
}

@article{majmudarVoterModelsExternal,
  title = {Voter Models and External Influence},
  author = {Majmudar, Jimit R. and Krone, Stephen M. and Baumgaertner, Bert O. and Tyson, Rebecca C.},
  doi = {10.1080/0022250X.2019.1625349},
  abstract = {In this paper, we extend the voter model (VM) and the threshold voter model (TVM) to include external influences modeled as a jump process. We study the newly-formulated models both analytically and computationally, employing diffusion approximations and mean field approximations. We derive results pertaining to the probability of reaching consensus on a particular opinion and also the expected consensus time. We find that although including an external influence leads to a faster consensus in general, this effect is more pronounced in the VM as compared to the TVM. Our findings suggest the potential importance of external influences in addition to local interactions.},
  journal = {Journal of Mathematical Sociology}
}

@article{majmudarVoterModelsExternala,
  title = {Voter Models and External Influence},
  author = {Majmudar, Jimit R. and Krone, Stephen M. and Baumgaertner, Bert O. and Tyson, Rebecca C.},
  doi = {10.1080/0022250X.2019.1625349},
  abstract = {In this paper, we extend the voter model (VM) and the threshold voter model (TVM) to include external influences modeled as a jump process. We study the newly-formulated models both analytically and computationally, employing diffusion approximations and mean field approximations. We derive results pertaining to the probability of reaching consensus on a particular opinion and also the expected consensus time. We find that although including an external influence leads to a faster consensus in general, this effect is more pronounced in the VM as compared to the TVM. Our findings suggest the potential importance of external influences in addition to local interactions.},
  journal = {Journal of Mathematical Sociology}
}

@article{makarFormateAssayBody1975,
  title = {Formate Assay in Body Fluids: Application in Methanol Poisoning},
  shorttitle = {Formate Assay in Body Fluids},
  author = {Makar, A. B. and McMartin, K. E. and Palese, M. and Tephly, T. R.},
  year = {1975},
  month = jun,
  volume = {13},
  pages = {117--126},
  issn = {0006-2944},
  journal = {Biochemical Medicine},
  language = {eng},
  number = {2},
  pmid = {1}
}

@article{makoviLimitsVerificationPreventing,
  title = {The Limits of Verification in Preventing the Spread of False Information on Networks},
  author = {Makovi, Kinga and Mun, Manuel},
  pages = {33},
  abstract = {The spread of false information on social networks has garnered ample scientific and popular attention. To counteract this spread, verification of the truthfulness of information has been proposed as a key intervention. Using a behavioral experiment with over 2000 participants we analyze individuals' willingness to spread false information in a network. All individuals in the network have aligned incentives, making lying attractive, countering an explicit norm of truthtelling that we imposed. We investigate how verifying the truth, endogenously or exogenously, impacts the choices to lie or to adhere to the norm of truth-telling, compared to a setting without the possibility of verification. The three key take-aways are: (i) verification is only moderately effective in reducing the spread of lies; its effectivity is (ii) contingent on the agency of individuals to seek truth, and (iii) the exposure of liars, and not only the lies told. These suggest that verification is not a blanket solution. In order to enhance its effectivity, it should be combined with fostering a culture of truth-seeking and with information on who spreads lies, not only on the lies told.},
  file = {/home/aleksandar/Dropbox/Zotero/Makovi_Mun/Makovi_Mun_The limits of verification in preventing the spread of false information on.pdf},
  language = {en}
}

@article{malarzHeiderBalanceCellular2020,
  ids = {malarzHeiderBalanceCellular},
  title = {Towards the {{Heider}} Balance with a Cellular Automaton},
  author = {Malarz, Krzysztof and Wo{\l}oszyn, Maciej and Ku{\l}akowski, Krzysztof},
  year = {2020},
  month = oct,
  volume = {411},
  pages = {132556},
  issn = {0167-2789},
  doi = {10.1016/j.physd.2020.132556},
  abstract = {The state of structural balance (termed also `Heider balance') of a social network is often discussed in social psychology and sociophysics. In this state, actors at network nodes classify other individuals as enemies or friends. Hence, the network contains two kinds of links: positive and negative. Here a new cellular automaton is designed and investigated, which mimics the time evolution towards the structural balance. The automaton is deterministic and synchronous. The medium is the triangular lattice with some fraction f of links removed. We analyse the number of unbalanced triads (parameterized as `energy'), the frequencies of balanced triads and correlations between them. The time evolution enhances the local correlations of balanced triads. Local configurations of unbalanced triads are found which are blinking with period of two time steps.},
  file = {/home/aleksandar/Documents/Zotero/Malarz et al/Malarz et al_2020_Towards the Heider balance with a cellular automaton.pdf},
  journal = {Physica D: Nonlinear Phenomena},
  language = {en}
}

@article{malikRareEventsComplex2020,
  title = {Rare Events in Complex Systems: {{Understanding}} and Prediction},
  shorttitle = {Rare Events in Complex Systems},
  author = {Malik, Nishant and Ozturk, Ugur},
  year = {2020},
  month = sep,
  volume = {30},
  pages = {090401},
  publisher = {{American Institute of Physics}},
  issn = {1054-1500},
  doi = {10.1063/5.0024145},
  file = {/home/aleksandar/Documents/Zotero/Malik_Ozturk/Malik_Ozturk_2020_Rare events in complex systems.pdf;/home/aleksandar/Zotero/storage/VGPD5JNH/5.html},
  journal = {Chaos: An Interdisciplinary Journal of Nonlinear Science},
  number = {9}
}

@article{malkaAreCulturalEconomic2019,
  title = {Are Cultural and Economic Conservatism Positively Correlated? {{A}} Large-Scale Cross-National Test},
  shorttitle = {Are Cultural and Economic Conservatism Positively Correlated?},
  author = {Malka, Ariel and Lelkes, Yphtach and Soto, Christopher J.},
  year = {2019},
  volume = {49},
  pages = {1045--1069},
  publisher = {{Cambridge University Press}},
  journal = {British Journal of Political Science},
  number = {3}
}

@article{malkaMoreIdeologyConservative2010,
  title = {More than Ideology: {{Conservative}}\textendash Liberal Identity and Receptivity to Political Cues},
  shorttitle = {More than Ideology},
  author = {Malka, Ariel and Lelkes, Yphtach},
  year = {2010},
  volume = {23},
  pages = {156--188},
  publisher = {{Springer}},
  journal = {Social Justice Research},
  number = {2-3}
}

@article{malkaWhoOpenAuthoritarian2020,
  title = {Who {{Is Open}} to {{Authoritarian Governance}} within {{Western Democracies}}?},
  author = {Malka, Ariel and Lelkes, Yphtach and Bakker, Bert N. and Spivack, Eliyahu},
  year = {2020},
  month = sep,
  pages = {1--20},
  issn = {1537-5927, 1541-0986},
  doi = {10.1017/S1537592720002091},
  abstract = {Recent events have raised concern about potential threats to democracy within Western countries. If Western citizens who are open to authoritarian governance share a common set of political preferences, then authoritarian elites can attract mass coalitions that are willing to subvert democracy to achieve shared ideological goals. With this in mind, we explored which ideological groups are most open to authoritarian governance within Western general publics using World Values Survey data from fourteen Western democracies and three recent Latin American Public Opinion Project samples from Canada and the United States. Two key findings emerged. First, cultural conservatism was consistently associated with openness to authoritarian governance. Second, within half of the democracies studied, including all of the English-speaking ones, Western citizens holding a protection-based attitude package⸺combining cultural conservatism with left economic attitudes⸺were the most open to authoritarian governance. Within other countries, protection-based and consistently right-wing attitude packages were associated with similarly high levels of openness to authoritarian governance. We discuss implications for radical right populism and the possibility of splitting potentially undemocratic mass coalitions along economic lines.},
  file = {/home/aleksandar/Zotero/storage/AS8SSN66/Malka et al. - 2020 - Who Is Open to Authoritarian Governance within Wes.pdf},
  journal = {Perspectives on Politics},
  language = {en}
}

@article{malvestioInterplayCoreCommunity2020,
  title = {Interplay between \$\$k\$\$ k -Core and Community Structure in Complex Networks},
  author = {Malvestio, Irene and Cardillo, Alessio and Masuda, Naoki},
  year = {2020},
  month = sep,
  volume = {10},
  pages = {14702},
  publisher = {{Nature Publishing Group}},
  issn = {2045-2322},
  doi = {10.1038/s41598-020-71426-8},
  abstract = {The organisation of a network in a maximal set of nodes having at least k neighbours within the set, known as \$\$k\$\$k-core decomposition, has been used for studying various phenomena. It has been shown that nodes in the innermost \$\$k\$\$k-shells play a crucial role in contagion processes, emergence of consensus, and resilience of the system. It is known that the \$\$k\$\$k-core decomposition of many empirical networks cannot be explained by the degree of each node alone,or equivalently, random graph models that preserve the degree of each node (i.e., configuration model). Here we study the \$\$k\$\$k-core decomposition of some empirical networks as well as that of some randomised counterparts, and examine the extent to which the \$\$k\$\$k-shell structure of the networks can be accounted for by the community structure.We find that preserving the community structure in the randomisation process is crucial for generating networks whose \$\$k\$\$k-core decomposition is close to the empirical one. We also highlight the existence, in some networks, of a concentration of the nodes in the innermost \$\$k\$\$k-shells into a small number of communities.},
  copyright = {2020 The Author(s)},
  file = {/home/aleksandar/Zotero/storage/P66XWJWC/Malvestio et al_2020_Interplay between $$k$$ k -core and community structure in complex networks.pdf;/home/aleksandar/Zotero/storage/9DSCM9I5/s41598-020-71426-8.html},
  journal = {Scientific Reports},
  language = {en},
  number = {1}
}

@article{maninoInfluencingDynamicsCorrelateda,
  title = {Influencing the Dynamics of Correlated Opinions in the Voter Model on Multiplex Networks},
  author = {Manino, Edoardo and Moreno, Guillermo Romero and Cai, Zhongqi and Brede, Markus},
  pages = {1},
  file = {/home/aleksandar/Zotero/storage/ZA2H3UHL/Manino et al. - Inﬂuencing the dynamics of correlated opinions in .pdf},
  language = {en}
}

@inproceedings{mansouriCorrelationSegregationSocial2020,
  title = {Correlation of {{Segregation}} and {{Social Networks}}' {{Majority Opinion}} in the {{Social Impact Model}}},
  booktitle = {2020 6th {{International Conference}} on {{Web Research}} ({{ICWR}})},
  author = {Mansouri, Alireza and Taghiyareh, Fattaneh},
  year = {2020},
  month = apr,
  pages = {66--71},
  doi = {10.1109/ICWR49608.2020.9122279},
  abstract = {Everyone may influence others to change or persist in their current opinions via face-to-face or online communications. Predicting a society's majority opinion about a specific topic is an interesting challenge with many applications, e.g., predicting social movements, political votings, economical marketing. Among the various opinion formation models, the social impact model of opinion formation is very suitable for online social networks and online communities. In this model, three main factors affect a society's overall opinion: (1) the initial population of opinion groups, (2) the noise of the individuals to be persuaded or persist on their opinions, and (3) the topology of the network of interactions among individuals. In this research, to analyze the effect of segregation on the dynamics of opinion in the model, we assumed a noise-free model. Furthermore, the network of individuals is a scale-free network, and the initial population size of both opinion groups are the same with randomly assigned opinions. Using an agent-based modeling approach, we studied how the segregation of opinion groups may affect the dynamics of opinion formation. The results reveal that there is a strong correlation between segregation and the trend of society's opinion. It could be concluded from the results that if starting from the same population size in both opinion groups, it is expected that the more segregated opinion group dominates the less one and determines the majority opinion of the society.},
  file = {/home/aleksandar/Zotero/storage/T6EYP6VX/9122279.html}
}

@article{marcaccioliMaximumEntropyApproach2020,
  title = {Maximum Entropy Approach to Multivariate Time Series Randomization},
  author = {Marcaccioli, Riccardo and Livan, Giacomo},
  year = {2020},
  month = jun,
  volume = {10},
  pages = {10656},
  publisher = {{Nature Publishing Group}},
  issn = {2045-2322},
  doi = {10.1038/s41598-020-67536-y},
  abstract = {Natural and social multivariate systems are commonly studied through sets of simultaneous and time-spaced measurements of the observables that drive their dynamics, i.e., through sets of time series. Typically, this is done via hypothesis testing: the statistical properties of the empirical time series are tested against those expected under a suitable null hypothesis. This is a very challenging task in complex interacting systems, where statistical stability is often poor due to lack of stationarity and ergodicity. Here, we describe an unsupervised, data-driven framework to perform hypothesis testing in such situations. This consists of a statistical mechanical approach\textemdash analogous to the configuration model for networked systems\textemdash for ensembles of time series designed to preserve, on average, some of the statistical properties observed on an empirical set of time series. We showcase its possible applications with a case study on financial portfolio selection.},
  copyright = {2020 The Author(s)},
  file = {/home/aleksandar/Dropbox/Zotero/Marcaccioli_Livan/Marcaccioli_Livan_2020_Maximum entropy approach to multivariate time series randomization.pdf;/home/aleksandar/Zotero/storage/8GXJZR6H/s41598-020-67536-y.html},
  journal = {Scientific Reports},
  language = {en},
  number = {1}
}

@article{marchLogicalMathematicalBoxes2018,
  title = {On {{Logical}} and {{Mathematical Boxes}}: {{Does}} the {{Attitudinal Entropy Framework Expand Our Understanding}} of {{Attitudes}}?},
  shorttitle = {On {{Logical}} and {{Mathematical Boxes}}},
  author = {March, David S. and Olson, Michael A. and Gaertner, Lowell},
  year = {2018},
  month = oct,
  volume = {29},
  pages = {196--199},
  issn = {1047-840X, 1532-7965},
  doi = {10.1080/1047840X.2018.1537299},
  journal = {Psychological Inquiry},
  language = {en},
  number = {4}
}

@article{marcusNetworkDarkPersonality2018,
  title = {A Network of Dark Personality Traits: {{What}} Lies at the Heart of Darkness?},
  shorttitle = {A Network of Dark Personality Traits},
  author = {Marcus, David K. and Preszler, Jonathan and {Zeigler-Hill}, Virgil},
  year = {2018},
  month = apr,
  volume = {73},
  pages = {56--62},
  issn = {0092-6566},
  doi = {10.1016/j.jrp.2017.11.003},
  abstract = {The question of whether there is a common element at the core of the various dark personality traits (e.g., psychopathy, narcissism, Machiavellianism, spitefulness, aggressiveness) has been the subject of debate. Callousness, manipulativeness, and disagreeableness have all been nominated as possibly serving as the core of these dark traits. Network analysis, which graphically and quantitatively describes the centrality of various related traits, provides a novel technique for examining this issue. We estimated an association network and an Adaptive Least Absolute Shrinkage and Selection Operator network for two large samples, one college student sample (N = 2831) and one mixed college student and Mechanical Turk sample (N = 844). Interpersonal manipulation and callousness were the traits that were central to the networks.},
  journal = {Journal of Research in Personality},
  language = {en}
}

@article{marianiNetworkbasedRankingSocial2020,
  title = {Network-Based Ranking in Social Systems: Three Challenges},
  shorttitle = {Network-Based Ranking in Social Systems},
  author = {Mariani, Manuel S and L{\"u}, Linyuan},
  year = {2020},
  month = may,
  volume = {1},
  pages = {011001},
  issn = {2632-072X},
  doi = {10.1088/2632-072X/ab8a61},
  abstract = {Ranking algorithms are pervasive in our increasingly digitized societies, with important real-world applications including recommender systems, search engines, and influencer marketing practices. From a network science perspective, network-based ranking algorithms solve fundamental problems related to the identification of vital nodes for the stability and dynamics of a complex system. Despite the ubiquitous and successful applications of these algorithms, we argue that our understanding of their performance and their applications to real-world problems face three fundamental challenges: (1) rankings might be biased by various factors; (2) their effectiveness might be limited to specific problems; and (3) agents' decisions driven by rankings might result in potentially vicious feedback mechanisms and unhealthy systemic consequences. Methods rooted in network science and agent-based modeling can help us to understand and overcome these challenges.},
  file = {/home/aleksandar/Documents/Zotero/Mariani_Lü/Mariani_Lü_2020_Network-based ranking in social systems.pdf;/home/aleksandar/Dropbox/research/papers/marianiNetworkbasedRankingSocial2020.md},
  journal = {Journal of Physics: Complexity},
  language = {en},
  number = {1}
}

@misc{MarkovChainMonte,
  title = {Markov {{Chain Monte Carlo}} in {{Practice}}},
  file = {/home/aleksandar/Zotero/storage/PKPSG34R/markov_chain_monte_carlo.html},
  howpublished = {https://betanalpha.github.io/assets/case\_studies/markov\_chain\_monte\_carlo.html}
}

@article{markusReviewMeasurementTheory,
  title = {Review of {{Measurement Theory}} and {{Applications}} for the {{Social Sciences}}},
  author = {Markus, Keith A.},
  doi = {10.1007/s11336-018-9637-6},
  abstract = {Bandalos divides the book into three parts, beginning with {$<$}em{$>$}Instrument Development and Analysis{$<$}/em{$>$}. Chapter 1 provides a very brief introduction of basic concepts and historical background. The remaining chapters in this part cover norms and standardized scores, test development, cognitive items, noncognitive items, and classical item analysis. For myself, I prefer to cover test development before norms and standardized scores and have...},
  journal = {Psychometrika}
}

@article{markusReviewMeasurementTheorya,
  title = {Review of {{Measurement Theory}} and {{Applications}} for the {{Social Sciences}}},
  author = {Markus, Keith A.},
  doi = {10.1007/s11336-018-9637-6},
  abstract = {Bandalos divides the book into three parts, beginning with {$<$}em{$>$}Instrument Development and Analysis{$<$}/em{$>$}. Chapter 1 provides a very brief introduction of basic concepts and historical background. The remaining chapters in this part cover norms and standardized scores, test development, cognitive items, noncognitive items, and classical item analysis. For myself, I prefer to cover test development before norms and standardized scores and have...},
  journal = {Psychometrika}
}

@article{marquisImprintingMultilevelTheory2013,
  title = {Imprinting: {{Toward}} a {{Multilevel Theory}}},
  shorttitle = {Imprinting},
  author = {Marquis, Christopher and Tilcsik, Andr{\'a}s},
  year = {2013},
  month = jun,
  volume = {7},
  pages = {195--245},
  issn = {1941-6520, 1941-6067},
  doi = {10.5465/19416520.2013.766076},
  journal = {Academy of Management Annals},
  language = {en},
  number = {1}
}

@article{marshConfirmatoryFactorAnalysis,
  title = {Confirmatory {{Factor Analysis}} ({{CFA}}), {{Exploratory Structural Equation Modeling}} ({{ESEM}}), and {{Set}}-{{ESEM}}: {{Optimal Balance Between Goodness}} of {{Fit}} and {{Parsimony}}},
  author = {Marsh, Herbert W. and Guo, Jiesi and Dicke, Theresa and Parker, Philip D. and Craven, Rhonda G.},
  doi = {10.1080/00273171.2019.1602503},
  abstract = {CFAs of multidimensional constructs often fail to meet standards of good measurement (e.g., goodness-of-fit, measurement invariance, and well-differentiated factors). Exploratory structural equation modeling (ESEM) represents a compromise between exploratory factor analysis' (EFA) flexibility, and CFA/SEM's rigor and parsimony, but lacks parsimony (particularly in large models) and might confound constructs that need to be kept separate. In Set-ESEM, two or more a priori sets of constructs are modeled within a single model such that cross-loadings are permissible within the same set of factors (as in Full-ESEM) but are constrained to be zero for factors in different sets (as in CFA). The different sets can reflect the same set of constructs on multiple occasions, and/or different constructs measured within the same wave. Hence, Set-ESEM that represents a middle-ground between the flexibility of traditional-ESEM (hereafter referred to as Full-ESEM) and the rigor and parsimony of CFA/SEM. Thus, the purposes of this article are to provide an overview tutorial on Set-ESEM, juxtapose it with Full-ESEM, and to illustrate its application with simulated data and diverse "real" data applications with accessible, heuristic explanations of best practice.},
  journal = {Multivariate Behavioral Research}
}

@article{marshConfirmatoryFactorAnalysisa,
  title = {Confirmatory {{Factor Analysis}} ({{CFA}}), {{Exploratory Structural Equation Modeling}} ({{ESEM}}), and {{Set}}-{{ESEM}}: {{Optimal Balance Between Goodness}} of {{Fit}} and {{Parsimony}}},
  author = {Marsh, Herbert W. and Guo, Jiesi and Dicke, Theresa and Parker, Philip D. and Craven, Rhonda G.},
  doi = {10.1080/00273171.2019.1602503},
  abstract = {CFAs of multidimensional constructs often fail to meet standards of good measurement (e.g., goodness-of-fit, measurement invariance, and well-differentiated factors). Exploratory structural equation modeling (ESEM) represents a compromise between exploratory factor analysis' (EFA) flexibility, and CFA/SEM's rigor and parsimony, but lacks parsimony (particularly in large models) and might confound constructs that need to be kept separate. In Set-ESEM, two or more a priori sets of constructs are modeled within a single model such that cross-loadings are permissible within the same set of factors (as in Full-ESEM) but are constrained to be zero for factors in different sets (as in CFA). The different sets can reflect the same set of constructs on multiple occasions, and/or different constructs measured within the same wave. Hence, Set-ESEM that represents a middle-ground between the flexibility of traditional-ESEM (hereafter referred to as Full-ESEM) and the rigor and parsimony of CFA/SEM. Thus, the purposes of this article are to provide an overview tutorial on Set-ESEM, juxtapose it with Full-ESEM, and to illustrate its application with simulated data and diverse "real" data applications with accessible, heuristic explanations of best practice.},
  journal = {Multivariate Behavioral Research}
}

@article{marsmanBayesianInferenceLowrank2015,
  title = {Bayesian Inference for Low-Rank {{Ising}} Networks},
  author = {Marsman, Maarten and Maris, Gunter and Bechger, Timo and Glas, Cees},
  year = {2015},
  month = mar,
  volume = {5},
  pages = {1--7},
  issn = {2045-2322},
  doi = {10.1038/srep09050},
  abstract = {Estimating the structure of Ising networks is a notoriously difficult problem. We demonstrate that using a latent variable representation of the Ising network, we can employ a full-data-information approach to uncover the network structure. Thereby, only ignoring information encoded in the prior distribution (of the latent variables). The full-data-information approach avoids having to compute the partition function and is thus computationally feasible, even for networks with many nodes. We illustrate the full-data-information approach with the estimation of dense networks.},
  copyright = {2015 The Author(s)},
  file = {/home/aleksandar/Zotero/storage/APC2KLK7/srep09050.html},
  journal = {Scientific Reports},
  language = {en},
  number = {1}
}

@techreport{marsmanGrandUnifiedTheory2019,
  title = {Towards a {{Grand Unified Theory}} of {{Network Models}}},
  author = {Marsman, Maarten and Waldorp, Lourens and Borsboom, Denny},
  year = {2019},
  month = nov,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/n98qt},
  abstract = {Network models like the Ising model are increasingly used in psychological research. In a recent article published in this journal, Brusco, Steinley, Hoffman, Davis-Stober, and Wasserman (in press) provide a critical assessment of the conditions that underlie the Ising model and the eLasso method that is commonly used to estimate it. In this commentary, we show that their main criticisms are unfounded. First, where Brusco et al. suggest that Ising models have little to do with classical network models such as random graphs, we show that they can be fruitfully connected. Second, if one makes this connection it is immediately evident that Brusco et al.'s second criticism \textendash{} that the Ising model requires complete population homogeneity and does not allow for individual differences in network structure \textendash{} is incorrect. In particular, we establish that if every individual has their own network, and these individual differences instantiate a random graph model, the Ising model will hold in the population. Hence, population homogeneity is sufficient for the Ising model, but it is not necessary, as Brusco et al. suggest. Third, we address Brusco et al.'s criticism regarding the sparsity assumption that is made in common uses of the Ising model. We show that this criticism is misdirected, as it targets a particular estimation algorithm for the Ising model rather than the model itself. We also describe various established and validated approaches for estimating the Ising model for networks that violate the sparsity assumption. Finally, we outline important avenues for future research.},
  language = {en},
  type = {Preprint}
}

@techreport{marsmanIdiographicIsingModel2019,
  title = {The {{Idiographic Ising}} Model},
  author = {Marsman, Maarten},
  year = {2019},
  month = nov,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/h3ka5},
  abstract = {The Ising model is a graphical model that has played an essential role in the field of network psychometrics, where it has been used as a theoretical model to re-conceptualize psychometric concepts and as a statistical model for the analysis of psychological data. But in network psychometrics, the psychological data that are analyzed often come from cross-sectional applications, and the practice of using graphical models such as the Ising model to analyze these data has been heavily critiqued in the past few years. The primary voiced concern centers around the inability of the Ising model to express heterogeneity in the population, and the necessity to then assume that the population is homogeneous w.r.t. the network's structure in practice. But associations at the group-level may be entirely different from associations at the individual level, and it is unclear what the estimated relations from cross-sectional data imply for associations at the individual level. In this paper, an idiographic interpretation of the Ising model is developed that does not require that persons are exchangeable replications of a single topological structure. Working with a clear, formal connection between network relations at the individual- and the group level, we have unique topological structures that characterize individuals and aggregate into an Ising model cross-sectionally.},
  type = {Preprint}
}

@article{marsmanIntroductionNetworkPsychometrics2018,
  title = {An {{Introduction}} to {{Network Psychometrics}}: {{Relating Ising Network Models}} to {{Item Response Theory Models}}},
  shorttitle = {An {{Introduction}} to {{Network Psychometrics}}},
  author = {Marsman, M. and Borsboom, D. and Kruis, J. and Epskamp, S. and van Bork, R. and Waldorp, L. J. and van der Maas, H. L. J. and Maris, G.},
  year = {2018},
  month = jan,
  volume = {53},
  pages = {15--35},
  issn = {0027-3171},
  doi = {10.1080/00273171.2017.1379379},
  abstract = {In recent years, network models have been proposed as an alternative representation of psychometric constructs such as depression. In such models, the covariance between observables (e.g., symptoms like depressed mood, feelings of worthlessness, and guilt) is explained in terms of a pattern of causal interactions between these observables, which contrasts with classical interpretations in which the observables are conceptualized as the effects of a reflective latent variable. However, few investigations have been directed at the question how these different models relate to each other. To shed light on this issue, the current paper explores the relation between one of the most important network models\textemdash the Ising model from physics\textemdash and one of the most important latent variable models\textemdash the Item Response Theory (IRT) model from psychometrics. The Ising model describes the interaction between states of particles that are connected in a network, whereas the IRT model describes the probability distribution associated with item responses in a psychometric test as a function of a latent variable. Despite the divergent backgrounds of the models, we show a broad equivalence between them and also illustrate several opportunities that arise from this connection.},
  file = {/home/aleksandar/Dropbox/Zotero/Marsman et al/Marsman et al_2018_An Introduction to Network Psychometrics2.pdf},
  journal = {Multivariate Behavioral Research},
  number = {1},
  pmid = {29111774}
}

@article{marsmanNoteLargescaleLogistic2017,
  title = {A Note on Large-Scale Logistic Prediction: Using an Approximate Graphical Model to Deal with Collinearity and Missing Data},
  shorttitle = {A Note on Large-Scale Logistic Prediction},
  author = {Marsman, Maarten and Waldorp, Lourens and Maris, Gunter},
  year = {2017},
  month = jul,
  volume = {44},
  pages = {513--534},
  issn = {1349-6964},
  doi = {10.1007/s41237-017-0024-x},
  abstract = {Large-scale prediction problems are often plagued by correlated predictor variables and missing observations. We consider prediction settings in which logistic regression models are used and propose a novel approach to make accurate predictions even when predictor variables are highly correlated and only partly observed. Our approach comprises three steps: first, to overcome the collinearity issue, we propose to model the joint distribution of the outcome variable and the predictor variables using the Ising network model. Second, to render the application of Ising networks feasible, we use a latent variable representation to apply a low-rank approximation to the network's connectivity matrix. Finally, we propose an approximation to the latent variable distribution that is used in the representation to handle missing observations. We demonstrate our approach with numerical illustrations.},
  journal = {Behaviormetrika},
  language = {en},
  number = {2}
}

@article{marsmanRelationsNetworksRegression,
  title = {Relations between Networks, Regression, Partial Correlation, and Latent Variable Model},
  author = {Marsman, Lourens Waldorp Maarten},
  pages = {21},
  abstract = {The Gaussian graphical model (GGM) has become a popular tool for analyzing networks of psychological variables. In a recent paper in this journal, Forbes, Wright, Markon, and Krueger (FWMK) voiced the concern that GGMs that are estimated from partial correlations wrongfully remove the variance that is shared by its constituents. If true, this concern has grave consequences for the application of GGMs. Indeed, if partial correlations only capture the unique covariances, then the data that come from a unidimensional latent variable model ULVM should be associated with an empty network (no edges), as there are no unique covariances in a ULVM. We know that this cannot be true, which suggests that FWMK are missing something with their claim. We introduce a connection between the ULVM and the GGM and use that connection to prove that we find a fully-connected and not an empty network associated with a ULVM. We then use the relation between GGMs and linear regression to show that the partial correlation indeed does not remove the common variance.},
  file = {/home/aleksandar/Zotero/storage/F7X64DLJ/Marsman - Relations between networks, regression, partial co.pdf},
  language = {en}
}

@article{martinBayesianApproachDirected2019,
  title = {A {{Bayesian Approach}} to {{Directed Acyclic Graphs}} with a {{Candidate Graph}}},
  author = {Martin, Evan A. and Fu, Audrey Qiuyan},
  year = {2019},
  month = sep,
  abstract = {Directed acyclic graphs represent the dependence structure among multiple variables. When learning these graphs from data, different amounts of information may be available for different edges. Although many methods have been developed to learn the topology of these graphs, most of these methods do not provide a measure of the uncertainty in the inference. Here, we propose a Bayesian method, namely baycn (BAYesian Causal Network), to estimate the posterior probability of three states for each inferred edge: present with one direction (X \textrightarrow{} Y), present with the opposite direction (X {$\leftarrow$} Y), and absent. Unlike existing Bayesian methods, our method requires that the prior probabilities of the three states be specified, and therefore provides a coherent interpretation of the posterior probabilities. We develop a fast MetropolisHastings Markov chain Monte Carlo algorithm for the inference. To perform the inference efficiently, our algorithm takes as input a candidate graph, which may be the output of another graph inference method and may contain false edges. In simulation studies our method achieves high accuracy with small variation across different scenarios and is comparable or better than existing Bayesian methods.},
  archivePrefix = {arXiv},
  eprint = {1909.10678},
  eprinttype = {arxiv},
  journal = {arXiv:1909.10678 [stat]},
  language = {en},
  primaryClass = {stat}
}

@article{martinEGAGNMFramework,
  title = {The {{EGA}}+{{GNM}} Framework: {{An}} Integrative Approach to Modelling Behavioural Syndromes},
  author = {Martin, Jordan S. and Massen, Jorg J. M. and {\v S}lipogor, Vedrana and Bugnyar, Thomas and Jaeggi, Adrian V. and Koski, Sonja E.},
  doi = {10.1111/2041-210X.13100},
  abstract = {{$<$}style{$>$}             .inline-equation \{             overflow-x:auto;             \}         {$<$}/style{$><$}article{$>$}        {$<$}div class="abstract-group"{$>$}       {$<$}section class="article-section article-section\_\_abstract" lang="en" data-lang="en" id="section-1-en"{$>$}          {$<$}h2 id="d3239270" class="article-section\_\_header section\_\_title main abstractlang\_en main"{$>$}Abstract{$<$}/h2{$>$}          {$<$}div class="article-section\_\_content en main"{$>$}                          {$<$}p{$>$}                          {$<$}ol start="1" class=""{$>$}                                                {$<$}li{$>$}Behavioural syndromes refer to correlated suites of behavioural traits exhibiting consistent among-individual variation, i.e. personality. Factor analysis (FA) is currently the dominant method for modelling behavioural syndromes in humans and animals. Although FA is useful for inferring the latent causes underlying trait correlations, it does not account for the pairwise behavioural interactions that also contribute to syndrome structure. Given that latent factors and pairwise interactions are likely ubiquitous causes of trait covariation, both should be modelled simultaneously. Currently, however, behavioural ecologists lack an integrative framework for describing and inferring such behavioural syndromes.{$<$}/li{$>$}                                                {$<$}li{$>$}Generalized network modelling (GNM), representing an integration of FA and Gaussian graphical modelling (GGM), meets this challenge. We provide a theoretical introduction to GNM as well as a method for detecting latent factors in GGMs called exploratory graph analysis (EGA). We then propose the novel EGA+GNM framework for modelling multiple sources of trait correlations and ensuring more robust causal inferences. To empirically demonstrate the utility of this framework, we compare models derived from EGA+GNM and FA using observational measures of social and arousal behaviour in common marmosets \emph{Callithrix jacchus}.                {$<$}/li{$>$}                                                {$<$}li{$>$}Using information-theoretic model comparison, we find support for EGA+GNM models compared to models generated by FA. Two EGA+GNM models suggest that while latent factors contribute to the emergence of clustered sociability and arousal behaviours, correlations among these traits may also be partially explained by pairwise interactions. Additionally, these behavioural clusters are hypothesized to be causally linked by a positive pairwise interaction between allogrooming and activity level.{$<$}/li{$>$}                                                {$<$}li{$>$}These results support our claim that EGA+GNM provides a superior and integrative framework for describing behavioural syndromes. Consequently, by simultaneously modelling both latent factors and pairwise interactions, behavioural ecologists can better understand the evolutionary causes and consequences of animal personality. A formal overview of the EGA+GNM framework and a R tutorial demonstrating its application are provided in the electronic {$<$}a class="suppLink scrollableLink" href="\#support-information-section"{$>$}Supporting Information{$<$}/a{$>$}.                {$<$}/li{$>$}                             {$<$}/ol{$>$}             {$<$}/p{$>$}},
  journal = {Methods in Ecology and Evolution}
}

@article{martinEGAGNMFrameworka,
  title = {The {{EGA}}+{{GNM}} Framework: {{An}} Integrative Approach to Modelling Behavioural Syndromes},
  author = {Martin, Jordan S. and Massen, Jorg J. M. and {\v S}lipogor, Vedrana and Bugnyar, Thomas and Jaeggi, Adrian V. and Koski, Sonja E.},
  doi = {10.1111/2041-210X.13100},
  abstract = {{$<$}style{$>$}             .inline-equation \{             overflow-x:auto;             \}         {$<$}/style{$><$}article{$>$}        {$<$}div class="abstract-group"{$>$}       {$<$}section class="article-section article-section\_\_abstract" lang="en" data-lang="en" id="section-1-en"{$>$}          {$<$}h2 id="d3239270" class="article-section\_\_header section\_\_title main abstractlang\_en main"{$>$}Abstract{$<$}/h2{$>$}          {$<$}div class="article-section\_\_content en main"{$>$}                          {$<$}p{$>$}                          {$<$}ol start="1" class=""{$>$}                                                {$<$}li{$>$}Behavioural syndromes refer to correlated suites of behavioural traits exhibiting consistent among-individual variation, i.e. personality. Factor analysis (FA) is currently the dominant method for modelling behavioural syndromes in humans and animals. Although FA is useful for inferring the latent causes underlying trait correlations, it does not account for the pairwise behavioural interactions that also contribute to syndrome structure. Given that latent factors and pairwise interactions are likely ubiquitous causes of trait covariation, both should be modelled simultaneously. Currently, however, behavioural ecologists lack an integrative framework for describing and inferring such behavioural syndromes.{$<$}/li{$>$}                                                {$<$}li{$>$}Generalized network modelling (GNM), representing an integration of FA and Gaussian graphical modelling (GGM), meets this challenge. We provide a theoretical introduction to GNM as well as a method for detecting latent factors in GGMs called exploratory graph analysis (EGA). We then propose the novel EGA+GNM framework for modelling multiple sources of trait correlations and ensuring more robust causal inferences. To empirically demonstrate the utility of this framework, we compare models derived from EGA+GNM and FA using observational measures of social and arousal behaviour in common marmosets \emph{Callithrix jacchus}.                {$<$}/li{$>$}                                                {$<$}li{$>$}Using information-theoretic model comparison, we find support for EGA+GNM models compared to models generated by FA. Two EGA+GNM models suggest that while latent factors contribute to the emergence of clustered sociability and arousal behaviours, correlations among these traits may also be partially explained by pairwise interactions. Additionally, these behavioural clusters are hypothesized to be causally linked by a positive pairwise interaction between allogrooming and activity level.{$<$}/li{$>$}                                                {$<$}li{$>$}These results support our claim that EGA+GNM provides a superior and integrative framework for describing behavioural syndromes. Consequently, by simultaneously modelling both latent factors and pairwise interactions, behavioural ecologists can better understand the evolutionary causes and consequences of animal personality. A formal overview of the EGA+GNM framework and a R tutorial demonstrating its application are provided in the electronic {$<$}a class="suppLink scrollableLink" href="\#support-information-section"{$>$}Supporting Information{$<$}/a{$>$}.                {$<$}/li{$>$}                             {$<$}/ol{$>$}             {$<$}/p{$>$}},
  journal = {Methods in Ecology and Evolution}
}

@article{martinez-sanchezConflictDevelopment,
  title = {Conflict and {{Development}}},
  author = {{Martinez-Sanchez}, Francisco},
  doi = {10.1142/S0219198920500073},
  abstract = {We examine a dynamic two-stage incumbent-challenger model of internal conflict, where the government, i.e., the incumbent, is in power, while the rebel challenges the government in a bid to capture state power. The central issue is the trade-off between development and security-based measures in countering such rebellion activity. We find that while an exogenous \emph{increase} in development decreases the level of rebellion activity, it \emph{increases} the level of security-based measures by the government whenever the rebel is

license: MIT

repository: atomashevic/website-at

license_type: permissive

repo_name: atomashevic/website-at

branch_name: refs/heads/master

extension: bib