Abstract

Relational event models are becoming increasingly popular in modeling temporal dynamics of social networks. Due to their nature of combining survival analysis with network model terms, standard methods of assessing model fit are not suitable to determine if the models are specified sufficiently to prevent biased estimates. This paper tackles this problem by presenting a simple procedure for model-based simulations of relational events. Predictions are made based on survival probabilities and can be used to simulate new event sequences. Comparing these simulated event sequences to the original event sequence allows for in depth model comparisons (including parameter as well as model specifications) and testing of whether the model can replicate network characteristics sufficiently to allow for unbiased estimates.

Abstract

Multi-edge networks capture repeated interactions between individuals. In social networks, such edges often form closed triangles, or triads. Standard approaches to measure this triadic closure, however, fail for multi-edge networks, because they do not consider that triads can be formed by edges of different multiplicity. We propose a novel measure of triadic closure for multi-edge networks of social interactions based on a shared partner statistic. We demonstrate that our operalization is able to detect meaningful closure in synthetic and empirical multi-edge networks, where common approaches fail. This is a cornerstone in driving inferential network analyses from the analysis of binary networks towards the analyses of multi-edge and weighted networks, which offer a more realistic representation of social interactions and relations.

Abstract

The norm of reciprocity shapes social interactions over time and should therefore be analyzed using longitudinal designs. This paper examines the temporal dynamics of reciprocity in the setting of legislative cosponsorship in the 113th U.S. Congress (2013–2015). Rather than aggregating cosponsoring events into network snapshots, cosponsoring events are ordered in time and reflect an event sequence of members supporting bills at distinct points in time. The norm of reciprocity is tested to see if it affects (a) new collaboration clusters emerging over time or (b) the time-to-cosponsoring. Findings suggest that receiving cosponsoring support recently, leads to future collaborations. However, this finding holds only for Republican members of Congress. Furthermore, reciprocity demands do not expedite cosponsorship support and members do not react faster to favors they received, calling mitigating effects of reciprocity on collective action into question.

Abstract

The Treaty of Lisbon strengthened the role of national parliaments in the European Union. It introduced an ‘early warning system’, granting parliamentary chambers the right to reject legislative proposals by the European Commission. Previous studies assumed independence between the decisions of parliaments to reject a legislative proposal. We apply recent advances in inferential network analysis and argue that parliamentary vetoes are better explained by conceptualizing parliaments’ veto actions as a temporal network. Network effects can be observed along the dimension of party families. Based on a new permutation approach, we find that parliaments with similar party majorities influence each other over the course of the decision period (‘social influence’), rather than basing their decisions independently on joint prior partisanship (‘selection’).