Community Detection in Large-Scale Complex Networks via Structural Entropy Game

2025-01-25

AI Summary

To address the inefficiency and difficulty in identifying overlapping communities in large-scale weighted directed networks, this paper proposes CoDeSEG, a heuristic algorithm based on two-dimensional structural entropy博弈. Methodologically, it pioneers the integration of structural entropy into a potential game framework to model community evolution, designing a local strategy-updating mechanism and a structural-entropy-driven node-overlap determination rule. Theoretically, CoDeSEG guarantees near-linear time complexity, overcoming traditional methods’ reliance on undirected or unweighted graphs. Extensive experiments on multiple real-world heterogeneous networks demonstrate that CoDeSEG achieves the fastest runtime while attaining state-of-the-art performance in overlapping normalized mutual information (ONMI) and F1-score. It thus significantly advances efficient, accurate, and scalable detection of overlapping communities in ultra-large-scale complex networks.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Game Theory and Economic Paradigms: Cooperative Game Theory
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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 issues, we propose a novel heuristic community detection algorithm, termed CoDeSEG, which identifies communities by minimizing the two-dimensional (2D) structural entropy of the network within a potential game framework. In the game, nodes decide to stay in current community or move to another based on a strategy that maximizes the 2D structural entropy utility function. Additionally, we introduce a structural entropy-based node overlapping heuristic for detecting overlapping communities, with a near-linear time complexity.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 score.

Problem

• Community Detection
• Large-scale Complex Networks
• Overlapping Communities

Innovation

• CoDeSEG
• Overlapping Community Detection
• Structural Entropy Optimization