Linking Through Time: Memory-Enhanced Community Discovery in Temporal Networks

Abstract

Temporal Networks, and more specifically, Markovian Temporal Networks, present a unique challenge regarding the community discovery task. The inherent dynamism of these systems requires an intricate understanding of memory effects and structural heterogeneity, which are often key drivers of network evolution. In this study, we address these aspects by introducing an innovative approach to community detection, centered around a novel modularity function. We focus on demonstrating the improvements our new approach brings to a fundamental aspect of community detection: the detectability threshold problem. We show that by associating memory directly with nodes' memberships and considering it in the expression of the modularity, the detectability threshold can be lowered with respect to cases where memory is not considered, thereby enhancing the quality of the communities discovered. To validate our approach, we carry out extensive numerical simulations, assessing the effectiveness of our method in a controlled setting. Furthermore, we apply our method to real-world data to underscore its practicality and robustness. This application not only demonstrates the method's effectiveness but also reveals its capacity to indirectly tackle additional challenges, such as determining the optimal time window for aggregating data in dynamic graphs. This illustrates the method's versatility in addressing complex aspects of temporal network analysis.