Community Detection in Large-Scale Complex Networks via Structural Entropy Game
Track: Graph algorithms and modeling for the Web
Keywords: Community Detection, Large- scale Networks, Structural Entropy, Potential Games
Abstract: Community detection is a critical task in graph theory, social network analysis, and bioinformatics, where communities are defined as clusters of densely interconnected nodes. However, detecting communities in large-scale networks with millions of nodes and billions of edges remains challenging due to the inefficiency and unreliability of existing methods. Moreover, many current approaches are limited to specific graph types, such as unweighted or undirected graphs, reducing their broader applicability. To address these limitations, we propose a novel heuristic community detection algorithm inspired by game theory, termed \framework, which identifies communities by minimizing the network's 2-dimensional (2D) structural entropy. In this potential game model, nodes decide whether to stay or transfer to another community based on a strategy that maximizes a 2D structural entropy utility function. Additionally, we introduce a structural entropy-based node overlapping heuristic to detect overlapping communities. The algorithm operates with near-linear time complexity, enabling efficient community detection in large-scale networks. Experimental results on real-world networks demonstrate that CoDeSEG is the fastest method available and achieves state-of-the-art performance in overlapping normalized mutual information (ONMI) and F1 scores.
Submission Number: 321
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