Community dynamics in connected time-dependent multilayer networks

TL;DR

This paper extends community detection methods to time-dependent multilayer networks and applies it to analyze Twitter data related to the 2015 Milan Expo, revealing dynamic social media community structures.

Contribution

It introduces a novel approach for community detection in complex, time-dependent multilayer networks with non-trivial inter-layer connections.

Findings

Effective modeling of social media community dynamics over time

Application to 400K Twitter posts during the Milan Expo

Insights into how communities evolve in response to large events

Abstract

Different strategies have been considered to extract information from social media about how similarly people react to the same news or event. In this context, a powerful method is offered by the application of graph techniques to the contents produced by social network users. In particular, large events typically attract enough content traffic along time to enable an analysis that explicitly models a dependence from the time dimension. Here we demonstrate how it is possible to extend the application of community detection strategies in complex networks to the case of time-dependent multilayer networks, whenever the connection between consecutive time layers is non-trivial. We apply the method to 400K Twitter post related to the Expo event held in Milan (Italy) between May and October 2015.