From temporal network data to the dynamics of social relationships

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 a...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2021-09
Main Authors: Gelardi, Valeria, Didier Le Bail, Barrat, Alain, Claidière, Nicolas
Format: Article
Language:English
Subjects:
Datasets
Evolution
Perturbation
Representations
Social factors
Social networks
Windows (intervals)
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2103.11755