On the Interplay Between Individuals’ Evolving Interaction Patterns and Traits in Dynamic Multiplex Social Networks

The interplay between individuals' social interactions and traits has been studied extensively but traditionally from static or homogeneous social network perspectives. The recent availability of dynamic and heterogeneous (multiplex) network data has introduced a variety of new challenges. Critically, novel computational models are needed that can cope with data dynamics and heterogeneity. In this paper, we introduce a computational framework that is broadly applicable to a variety of dynamic, multiplex domains, which focuses on: 1) measuring changes in node interaction patterns with time, 2) clustering nodes with similar evolving patterns, and 3) linking the clusters with trait similarities. We apply the framework to study the interplay between evolving topology and traits in an 18-month social network dataset encompassing both digital communications and co-location instances. Notably, we demonstrate how our framework captures results that would otherwise be missed by a simpler approach such as static network analysis alone. In addition, we uncover network-trait interplays that have not been studied to date and could lead to novel insights by domain scientists.