From temporal network data to the dynamics of social relationships

TL;DR

This paper introduces a novel framework for transforming temporal social network data into an evolving weighted network that accounts for social relationship interdependence and individual attention limits, improving analysis of social dynamics.

Contribution

The authors propose a new method to convert temporal networks into evolving weighted networks considering social interdependence and attention constraints, enhancing the understanding of social dynamics.

Findings

Framework highlights structural and temporal features of social networks.

Method detects social perturbations across multiple timescales.

Application to real and synthetic data demonstrates effectiveness.

Abstract

Networks are well-established representations of social systems, and temporal networks are widely used to study their dynamics. Temporal network data often consist in a succession of static networks over consecutive time windows whose length, however, is arbitrary, not necessarily corresponding to any intrinsic timescale of the system. Moreover, the resulting view of social network evolution is unsatisfactory: short time windows contain little information, whereas aggregating over large time windows blurs the dynamics. Going from a temporal network to a meaningful evolving representation of a social network therefore remains a challenge. Here we introduce a framework to that purpose: transforming temporal network data into an evolving weighted network where the weights of the links between individuals are updated at every interaction. Most importantly, this transformation takes into account the interdependence of social relationships due to the finite attention capacities of individuals: each interaction between two individuals not only reinforces their mutual relationship but also weakens their relationships with others. We study a concrete example of such a transformation and apply it to several data sets of social interactions. Using temporal contact data collected in schools, we show how our framework highlights specificities in their structure and temporal organization. We then introduce a synthetic perturbation into a data set of interactions in a group of baboons to show that it is possible to detect a perturbation in a social group on a wide range of timescales and parameters. Our framework brings new perspectives to the analysis of temporal social networks.