While the economic benefits of internal migration are widely documented, the social costs of internal migration have received comparatively less attention. In addition, most studies focus on the impact of the last-recorded migration, ignoring the cumulative impact of successive migrations. Grounded in the life-course trajectory approach to migration and the convoy model of social networks, this paper addresses this gap by applying sequence and cluster analysis to retrospective data from the Survey of Health, Ageing and Retirement in Europe (SHARE) in 26 European countries to establish internal migration trajectories based on the timing, frequency, and direction of migration between NUTS-2 regions. The results reveal that differences in social networks between lifetime stayers, childhood migrants and one-time adult migrants are minimal. A more complex picture emerges for repeat migrants who account for half migrants and are split between return migrants, serial onward migrants, and circular migrants. Regression results show that repeat migrants – whether onward, return, or circular – display social networks less focused on family and more geographically dispersed, which results in a lower frequency of contact than lifetime stayers. However, repeat migrants report the same level of overall satisfaction with their social networks as lifetime stayers, which suggests that they start with different expectations than stayers or simply adjust their expectations in response to the social costs and benefits of migration.

For different currents in policy analysis as policy networks and the Advocacy Coalition Framework (ACF), identifying coalitions from policy beliefs and coordination between actors is crucial to a precise understanding of a policy process. Focusing particularly the relational dimension of ACF approaches linked with policy network analysis, determining policy subsystems from the actor collaborations and exchanges has recently begun offering fertile links with the network analysis. Studies in this way frequently apply Block Modeling and Community Detection (BMCD) strategies to define homogeneous political groups. However, the BMCD literature is growing quickly, using a wide variety of algorithms and interesting selection methods that are much more diverse than those used in the policy network analysis and particularly the ACF when this current focused on the collaboration networks before or after regarding the belief distance between actors. Identifying the best methodological option in a specific context can therefore be difficult and few ACF studies give an explicit justification. On the other hand, few BMCD publications offer a systematic comparison of real social networks and they are never applied to policy network datasets. This paper offers a new, relevant 5-Step selection method to reconcile advances in both the policy networks/ACF and BMCD. Using an application based on original African policy network data collected in Madagascar and Niger, we provide a useful set of practical recommendations for future ACF studies using policy network analysis: (i) the density and size of the policy network affect the identification process, (ii) the “best algorithm” can be rigorously determined by maximizing a novel indicator based on convergence and homogeneity between algorithm results, (iii) researchers need to be careful with missing data: they affect the results and imputation does not solve the problem.

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.

Access to social capital is stratified by socioeconomic status and has been cross-sectionally linked to involvement in voluntary organizations. Yet, we know little about the origin and interplay of these empirical regularities. Regression analyses on German panel data (SC6-NEPS) reveal that people rich in social capital join organizations more often (selection). Furthermore, joiners access more and higher-status social capital after joining (socializing opportunities). Low-status individuals disproportionally extend their reach towards higher positions through involvement but join less often. Compared to a counterfactual situation in which nobody joins, current involvement patterns marginally reduce some socioeconomic inequalities in access to social capital.

Combining the results of two empirical studies, we investigate the role of alters’ motivation in explaining change in ego’s network position over time. People high in communal motives, who are prone to supportive and altruistic behavior in their interactions with others as a way to gain social acceptance, prefer to establish ties with co-workers occupying central positions in organizational social networks. This effect results in a systematic network centrality bias: The personal network of central individuals (individuals with many incoming ties from colleagues) is more likely to contain more supportive and altruistic people than the personal network of individuals who are less central (individuals with fewer incoming ties). This result opens the door to the possibility that the effects of centrality so frequently documented in empirical studies may be due, at least in part, to characteristics of the alters in an ego’s personal community, rather than to egos themselves. Our findings invite further empirical research on how alters’ motives affect the returns that people can reap from their personal networks in organizations.