From calls to communities: a model for time varying social networks

TL;DR

This paper introduces a temporal network model incorporating social reinforcement, focal closure, and cyclic closure to better understand the dynamics of social interactions and community formation, validated against real-world mobile phone data.

Contribution

The paper presents a novel activity-driven temporal network model with memory that captures key social mechanisms and replicates real-world social network features.

Findings

Model reproduces degree and weight distributions of real networks

Emergence of strong and weak ties with realistic correlations

Community structures similar to empirical social networks

Abstract

Social interactions vary in time and appear to be driven by intrinsic mechanisms, which in turn shape the emerging structure of the social network. Large-scale empirical observations of social interaction structure have become possible only recently, and modelling their dynamics is an actual challenge. Here we propose a temporal network model which builds on the framework of activity-driven time-varying networks with memory. The model also integrates key mechanisms that drive the formation of social ties - social reinforcement, focal closure and cyclic closure, which have been shown to give rise to community structure and the global connectedness of the network. We compare the proposed model with a real-world time-varying network of mobile phone communication and show that they share several characteristics from heterogeneous degrees and weights to rich community structure. Further, the strong and weak ties that emerge from the model follow similar weight-topology correlations as real-world social networks, including the role of weak ties.