Discovering Communities in Continuous-Time Temporal Networks by Optimizing L-Modularity
This addresses the need for temporally accurate community detection in dynamic networks, which is incremental as it adapts an existing modularity concept to continuous-time settings.
The paper tackles the problem of community detection in continuous-time temporal networks by introducing LAGO, a method that optimizes Longitudinal Modularity to capture exact temporal changes without discretization, demonstrating efficient performance on synthetic and real-world datasets.
Community detection is a fundamental problem in network analysis, with many applications in various fields. Extending community detection to the temporal setting with exact temporal accuracy, as required by real-world dynamic data, necessitates methods specifically adapted to the temporal nature of interactions. We introduce LAGO, a novel method for uncovering dynamic communities by greedy optimization of Longitudinal Modularity, a specific adaptation of Modularity for continuous-time networks. Unlike prior approaches that rely on time discretization or assume rigid community evolution, LAGO captures the precise moments when nodes enter and exit communities. We evaluate LAGO on synthetic benchmarks and real-world datasets, demonstrating its ability to efficiently uncover temporally and topologically coherent communities.