Exploring and comparing temporal clustering methods

Abstract

Description of temporal networks and detection of dynamic communities have been hot topics of research for the last decade. However, no consensual answers to these challenges have been found due to the complexity of the task. Static communities are not well defined objects, and adding a temporal dimension renders the description even more difficult. In this article, we propose a coherent temporal clustering method: the Best Combination of Local Communities (BCLC). Our method aims at finding a good balance between two conflicting objectives : closely following the short time evolution by finding optimal partitions at each time step and temporal smoothness, which privileges historical continuity. We test our algorithm on two bibliographic data sets by comparing their mesoscale dynamic description to those derived from a (static) simple clustering algorithm applied over the whole data set. We show that our clustering algorithm can reveal more complex dynamics than the simple approach and reach a good agreement with expert's knowledge.