Social Networks最新文献

This paper aims to understand how a group of academics cite each others’ work through time, considering the simultaneous co-evolution of three networks representing their scientific collaboration, the journals in which they publish and institutional membership. It argues that both social and cognitive processes contribute to these dynamics. Two types of network mechanisms are considered specifically: closures by affiliation and closures by association. To assess whether these mechanisms generate the macro features of the network under study, we propose new features for three-mode multilevel networks such as the mixed geodesic distances, mixed degree distributions, and the mixed quadrilateral census. We investigate whether a micro-level model that considers the above-mentioned network mechanisms is able to correctly reproduce these features. We apply stochastic actor-oriented models (SAOMs) for one-mode and two-mode networks to link the micro-macro processes using a dataset of a scientific community of astronomers from 2013 to 2015. The results suggest that social relationships grounded on scientific collaboration and proximity based on institutional affiliation are more accurately suited to understanding the co-evolution of the network of citations than an alternative approach that merely considers cognitive-based networks measured as the similarity in publishing in the same journals.

We examine the structural patterns in the cognitive representation of social networks by systematically classifying false positives and negatives. Although existing literature on Cognitive Social Structures (CSS) has begun exploring false positives and negatives by comparing actual and perceived networks, it has not differentiated simultaneous occurrences of true and false positives and negatives on network motifs, such as reciprocity and triadic closure. Here, we propose a theoretical framework to categorize three classes of errors we call imaginary network motifs as combinations of accurately and erroneously perceived ties: (a) partially false, (b) completely false, and (c) mixed false. Using four published CSS data sets, we empirically test which imaginary network motifs are significantly more or less present in different types of perceived networks than the corresponding actual networks. Our results confirm that people not only fill in the blanks as suggested in the prior research but also conceive other imaginary structures. The findings advance our understanding of perception gaps between actual and perceived networks and have implications for designing more accurate network modeling and sampling.

An abundance of centrality indices has been proposed which capture the importance of nodes in a network based on different structural features. While there remains a persistent belief that similarities in outcomes of indices is contingent on their technical definitions, a growing body of research shows that structural features affect observed similarities more than technicalities. We conduct a series of experiments on artificial networks to trace the influence of specific structural features on the similarity of indices which confirm previous results in the literature. Our analysis on 1163 real-world networks, however, shows that little of the observations on synthetic networks convincingly carry over to empirical settings. Our findings suggest that although it seems clear that (dis)similarities among centralities depend on structural properties of the network, using correlation type analyses do not seem to be a promising approach to uncover such connections.

The exponential random graph (ERGM) model is a commonly used statistical framework for studying the determinants of tie formations from social network data. To test scientific theories under ERGMs, statistical inferential techniques are generally used based on traditional significance testing using $p$-values. This methodology has certain limitations, however, such as its inconsistent behavior when the null hypothesis is true, its inability to quantify evidence in favor of a null hypothesis, and its inability to test multiple hypotheses with competing equality and/or order constraints on the parameters of interest in a direct manner. To tackle these shortcomings, this paper presents Bayes factors and posterior probabilities for testing scientific expectations under a Bayesian framework. The methodology is implemented in the R package BFpack. The applicability of the methodology is illustrated using empirical collaboration networks and policy networks.

Social relations are embedded in material, cultural, and institutional settings that affect network dynamics and the resulting topologies. For example, romantic entanglements are subject to social and cultural norms, interfirm alliances are constrained by country-specific legislation, and adolescent friendships are conditioned by classroom settings and neighborhood effects. In short, social contexts shape social relations and the networks they give rise to. However, how and when they do so remain to be established. This paper presents network ecology as a general framework for identifying how the proximal environment shapes social networks by focusing interactions and social relations, and how these interactions and relations in turn shape the environment in which social networks form. Tie fitness is introduced as a metric that quantifies how well particular dyadic social relations would align with the setting. Using longitudinal networks collected on two cohorts each in 18 North American schools, i.e., 36 settings, we develop five generalizable observations about the time-varying fitness of adolescent friendship. Across all 252 analyzed networks, tie fitness predicted new tie formation, tie longevity, and tie survival. Dormant fit ties cluster in relational niches, thereby establishing a resource base for social identities competing for increased representation in the relational system.

We study the evolution of cooperation in the Volunteer’s Dilemma using the stochastic Moran process on dynamic graphs, which models a birth–death dynamic on structured finite populations. According to the Moran process, in each period one player is selected to reproduce, where the probability of being selected is proportional to payoff-related fitness levels, and a copy of this player is substituted for a player who is randomly selected to die. The interaction of the players is embedded in a network structure which determines the overlapping groups within which the Volunteer’s Dilemma is played. Networks vary to the extent they exhibit homophily, i.e., they vary in the extent to which the interacting groups primarily encompass either cooperators or defectors instead of a mix of both types of players. By varying the degree of homophily in the network, we thus can study the question if and to what extent assortment of strategies favors the evolution of cooperation in the Volunteer’s Dilemma. Our results show that a surprisingly high extent of homophily is required to ensure the evolution of cooperation in the Volunteer’s Dilemma when modeled as a stochastic process in pure strategies. Other parameters, such as selection pressure or the number of initial cooperators, have a comparatively small effect on the fixation of cooperation in the population.

Longitudinal multivariate social network analysis (N=3692 adolescents; 136 classrooms) showed that adolescents were more likely to perceive their friends as popular but did not choose peers that they perceived to be popular as friends. Adolescents aligned their perceptions of popularity with their friends. Adolescents who received many popularity nominations attracted more popularity but not more friendship. Friends of peers seen as popular by an individual were more likely to be seen as popular by that individual. These results highlight the importance of describing popularity as a network, exploring dyadic, triadic, and group processes separately.

Research examining co-offending has become increasingly popular over the last two decades. Despite this, there remains a dearth of research examining the dynamics of co-offending across time, largely due to limited access to longitudinal data. In the current paper we are interested in explaining crime versatility, and therefore we employ Relational Hyperevent Models (RHEM) to model the conditional probability that a given group of co-offenders engages in one set of crime categories rather than another. Thus, we are analyzing a two-mode network (actors by crime categories) and explain, conditional on a given group of co-offenders, their participation in the set of specific crime types involved in a particular crime event. With respect to co-offending, results reveal that, compared with solo offenders, groups of two or more co-offenders are more likely to engage in crime events involving more than just one crime category. Results suggest that in the context of co-offending both market and property crime show evidence of differential association and social learning. Naïve partners in co-offending partnerships learn the skills and knowledge needed to participate in co-offending involving market and property crime.

While poor performance is one reason employees are fired, previous research suggests it plays a limited role in explaining terminations. We argue that sociopolitical concerns play a role in determining who is terminated. Using field data from a U.S. health care company and experimental data using participants with supervisory experience, we show how the supervisor’s political concerns with the focal employee, which are contingent on the supervisor-employee political relationship and the way it is embedded within the workplace network, are related to dismissal decisions. Not only do we expect that a supervisor will be less likely to terminate an employee they see as a political ally and more likely to dismiss an adversary, but we also argue that a supervisor with fewer (more) alternative allies to the employee is less (more) likely to dismiss the employee. Additionally, a supervisor with numerous adversaries in their own network depends more heavily on the employee politically, making dismissal less likely, whereas if the employee has numerous adversaries, the supervisor has greater latitude to terminate the employee. Our findings contribute to research on involuntary turnover by showing that a social network approach to understanding organizational politics helps us understand why specific individuals are targeted for dismissal, above and beyond performance considerations.