Comprehensive, Efficient Large-Scale Community Detection via Structural Entropy Game

2026-07-15

AI Summary

This work addresses the limitations of existing community detection methods, which often suffer from low efficiency and poor generalization when applied to large-scale networks with tens of millions of nodes and billions of edges, and struggle to uniformly support non-overlapping, overlapping, and dynamic community structures. To overcome these challenges, the authors propose CoDeSEG, a novel algorithm that unifies two-dimensional structural entropy with potential game theory within a single framework. In this framework, nodes autonomously select communities by maximizing a structural entropy–based utility function. The method further incorporates an overlapping heuristic strategy with near-linear time complexity and a cascading influence propagation mechanism to enable efficient and adaptive dynamic updates. Evaluated on 14 large-scale real-world networks, CoDeSEG achieves state-of-the-art performance across all three community detection tasks, significantly improving both accuracy and computational efficiency.

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 existing methods are limited to specific types of graph structures (such as unweighted or undirected graphs) or are designed solely for detecting static communities, reducing their broader applicability. To address these issues, we propose a novel heuristic community detection algorithm, termed CoDeSEG, which identifies communities by minimizing the network's two-dimensional (2D) structural entropy within a potential game framework. In the game, nodes decide to stay in the 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. Furthermore, we design a cascading influence propagation-based adaptive community update strategy, which dynamically identifies and processes nodes whose community affiliations may change during graph evolution, thereby effectively extending CoDeSEG to dynamic community detection scenarios. Experimental results on fourteen large-scale networks demonstrate that CoDeSEG achieves state-of-the-art performance across three community detection tasks (overlapping, non-overlapping, dynamic), while also delivering substantial improvements in detection efficiency.

Problem

• community detection
• large-scale networks
• structural entropy
• dynamic graphs
• overlapping communities

Innovation

• structural entropy
• potential game
• overlapping communities
• dynamic community detection
• near-linear time complexity