Longitudinal Modularity, a Modularity for Link Streams

TL;DR

This paper introduces a novel Modularity quality function adapted for link streams, enabling temporal community detection independent of time scale, and demonstrates its effectiveness for dynamic community evaluation.

Contribution

It presents the first adaptation of Modularity for link streams, bridging static and dynamic community detection methods and addressing temporal scale independence.

Findings

The proposed Modularity function effectively evaluates dynamic communities.

It is independent of the temporal resolution of link streams.

Experimental results validate its relevance for dynamic community analysis.

Abstract

Temporal networks are commonly used to model real-life phenomena. When these phenomena represent interactions and are captured at a fine-grained temporal resolution, they are modeled as link streams. Community detection is an essential network analysis task. Although many methods exist for static networks, and some methods have been developed for temporal networks represented as sequences of snapshots, few works can handle link streams. This article introduces the first adaptation of the well-known Modularity quality function to link streams. Unlike existing methods, it is independent of the time scale of analysis. After introducing the quality function, and its relation to existing static and dynamic definitions of Modularity, we show experimentally its relevance for dynamic community evaluation.