Social Networks最新文献

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.

Much of the theory of estimation for exponential family models, which include exponential-family random graph models (ERGMs) as a special case, is well-established and maximum likelihood estimates (MLEs) in particular enjoy many desirable properties. However, in the case of many ERGMs, direct calculation of MLEs is impossible and therefore methods for approximating MLEs and/or alternative estimation methods must be employed. Many MLE approximation algorithms require an alternative estimate as a starting point. The maximum pseudo-likelihood estimator (MPLE) is frequently taken as this starting point. Here, we discuss a potentially large class of such alternatives based on the fact that, unlike the MLE, the MPLE fails to satisfy the so-called “likelihood principle”. This means that different networks may have different MPLEs even if they have the same sufficient statistics. We exploit this fact here to search for improved starting values for approximation-based MLE methods. The method we propose has shown its merit in producing an MLE for a network dataset and model that had defied estimation using all other known methods.

Network data uniquely allow –relationships to be multiply reported, creating varying rates of relationship nomination reciprocation. However, what drives such variation is unclear. Variation in reciprocation may reflect substantive information about relationships (e.g., social salience or desirability) or study design (e.g., question wording or capped nominations). We examine predictors of nomination reciprocity in romantic network data from the PROSPER study to analyze individual and dyadic predictors of nomination reciprocity. Results show higher grades predict higher reciprocity, while same-sex relationships and behaviorally discordant dyads are less likely to be reciprocal.

Venture capital (VC) is a relatively newly emergent industry that is still subject to large uncertainties in China. Therefore, building a robust social network with other VC institutions is a good way to share information, various resources, and benefit from skill and knowledge complementarity to against risks. Strong evidences indicate that better networked VC institutions are of a better financial performance, however, most of previous works overlook the evolution of VC institutions and only focus on some simple topology indicators of the static syndication network, which also neglects higher-order network structure and cannot give a comprehensive evaluation. In this paper, based on VC investment records in the Chinese market, we construct temporal syndication networks between VC institutions year by year. As k-shell decomposition considers higher-order connection patterns, we employ k-shell as an evaluation of the influence of VC institutions in syndication networks. By clustering time series of k-shell values, the VC institutions in China fall into five groups that are quite different from each other on financial performances and investment behaviors. This, in turn, proves the power of our method that only based on proper sequential network properties, we can reveal their financial investment performance. Compared to other network centrality measurements, k-shell is a better indicator that is indicated by a smaller intra-group distance and a larger inter-group distance.

The extent to which a policy actor is perceived as being influential by others can shape their role in a policy process. The interest group literature has examined how the use of advocacy tactics, such as lobbying or media campaigns, contributes to an actor’s perceived influence. The policy networks literature, in turn, has found that network ties and occupying certain institutional roles can explain why actors are perceived as influential. When investigating what explains perceptions of influence, interest groups scholars have not accounted for network interdependencies and network scholars have so far not examined the advocacy tactics used by interest groups. This paper addresses the gap at the intersection of these two literatures by investigating the relationship between network ties, institutional roles, advocacy tactics and the presence of influence attribution ties in climate change policy networks. Exponential random graph models are applied to network data collected from the organisations participating in the national climate change policymaking processes in six EU countries that vary by the extent to which they are majoritarian or consensual democracies: Czechia, Finland, Germany, Ireland, Portugal, and Sweden. The results show that network ties and institutional roles are better predictors of influence attribution ties than advocacy tactics and that there is no pattern in the relationship between advocacy tactics and influence attribution ties across different institutional contexts. These findings suggest that because influence is primarily associated with structural factors (network ties and institutional roles) that more established policy actors are likely to have more influence, which may inhibit the need for a significant step change in climate policies.