Longitudinal Modularity, a Modularity for Link Streams
This work addresses community detection for link streams, a specific type of temporal network, but is incremental as it adapts an existing method.
The authors tackled the problem of community detection in link streams, which model fine-grained temporal interactions, by introducing the first adaptation of Modularity to link streams, showing its relevance for dynamic community evaluation.
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.