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. However, going from temporal network data (i.e. a stream of interactions between individuals) to a representation of the social group's evolution remains a challenge....

Full description

Saved in:
Bibliographic Details
Published in:Proceedings of the Royal Society. B, Biological sciences Biological sciences, 2021-09, Vol.288 (1959), p.20211164-20211164
Main Authors: Gelardi, Valeria, Le Bail, Didier, Barrat, Alain, Claidiere, Nicolas
Format: Article
Language:English
Subjects:
Animals
Behaviour
Condensed Matter
Interpersonal Relations
Physics
Social Networking
Statistical Mechanics
Citations: Items that this one cites
Items that cite this one
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. However, going from temporal network data (i.e. a stream of interactions between individuals) to a representation of the social group's evolution remains a challenge. Indeed, the temporal network at any specific time contains only the interactions taking place at that time and aggregating on successive time-windows also has important limitations. Here, we present a new framework to study the dynamic evolution of social networks based on the idea that social relationships are interdependent: as the time we can invest in social relationships is limited, reinforcing a relationship with someone is done at the expense of our relationships with others. We implement this interdependence in a parsimonious two-parameter model and apply it to several human and non-human primates' datasets to demonstrate that this model detects even small and short perturbations of the networks that cannot be detected using the standard technique of successive aggregated networks. Our model solves a long-standing problem by providing a simple and natural way to describe the dynamic evolution of social networks, with far-reaching consequences for the study of social networks and social evolution.
ISSN:0962-8452
1471-2954
DOI:10.1098/rspb.2021.1164