Discovering Communities in Continuous-Time Temporal Networks by Optimizing L-Modularity

2025-10-01

AI Summary

To address accuracy loss in community detection for continuous-time dynamic networks caused by temporal discretization, this paper proposes a continuous-time community evolution modeling framework based on Longitudinal Modularity (L-modularity) and introduces the greedy optimization algorithm LAGO. Unlike conventional approaches relying on time slicing, LAGO operates directly at the event-level temporal granularity, enabling non-rigid, fine-grained evolution of node community memberships. L-modularity uniquely unifies temporal continuity with modularity optimization, permitting precise identification of the exact timestamps when nodes join or leave communities. Experiments on synthetic and real-world datasets demonstrate that LAGO significantly improves both temporal resolution and community detection accuracy, while preserving topological consistency and temporal fidelity.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Social Networks and Social Media: Social media analysis through the lenses of networks

Abstract

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.

Problem

• Detecting communities in continuous-time temporal networks
• Optimizing longitudinal modularity for dynamic networks
• Capturing precise node entry/exit moments in communities

Innovation

• Greedy optimization of Longitudinal Modularity for communities
• Captures precise node entry and exit moments in communities
• Avoids time discretization and rigid evolution assumptions