Exploring and comparing temporal clustering methods

TL;DR

This paper introduces BCLC, a novel temporal clustering method for dynamic community detection in temporal networks, balancing local community accuracy and temporal smoothness for better community evolution tracking.

Contribution

The paper proposes BCLC, a new coherent temporal clustering approach that effectively balances short-term accuracy with historical continuity in dynamic community detection.

Findings

BCLC effectively captures evolving communities in temporal networks.

The method balances local community detection and temporal smoothness.

Demonstrates improved community tracking over existing 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.