Dynamical and bursty interactions in social networks

TL;DR

This paper introduces a flexible modeling framework for social contact networks that captures bursty, dynamic interactions with memory effects, enabling better analysis of processes like information spread and disease transmission.

Contribution

The paper presents a novel, analytically tractable model for dynamical, bursty social interactions incorporating memory effects, extending previous static or less detailed models.

Findings

Model captures bursty contact patterns with memory effects.

Framework allows analytical and numerical analysis of dynamic processes.

Extensible to various social interaction scenarios.

Abstract

We present a modeling framework for dynamical and bursty contact networks made of agents in social interaction. We consider agents' behavior at short time scales, in which the contact network is formed by disconnected cliques of different sizes. At each time a random agent can make a transition from being isolated to being part of a group, or vice-versa. Different distributions of contact times and inter-contact times between individuals are obtained by considering transition probabilities with memory effects, i.e. the transition probabilities for each agent depend both on its state (isolated or interacting) and on the time elapsed since the last change of state. The model lends itself to analytical and numerical investigations. The modeling framework can be easily extended, and paves the way for systematic investigations of dynamical processes occurring on rapidly evolving dynamical networks, such as the propagation of an information, or spreading of diseases.