通过结构熵博弈实现全面、高效的大规模社区检测
Comprehensive, Efficient Large-Scale Community Detection via Structural Entropy Game
AI总结：
该研究针对大规模网络社区检测难题，提出CoDeSEG算法，通过在潜在博弈框架内最小化二维结构熵识别社区，引入基于结构熵的节点重叠启发式方法及自适应社区更新策略，在多项社区检测任务中性能达最优，检测效率显著提升。
AI中文摘要：
社区检测在图论、社交网络分析和生物信息学中是一项关键任务，其中社区被定义为紧密相连节点的集群。然而，由于现有方法的低效和不可靠，在具有数百万节点和数十亿条边的大规模网络中检测社区仍然具有挑战性。此外，许多现有方法限于特定类型的图结构或仅用于检测静态社区。为解决这些问题，我们提出一种新颖的启发式社区检测算法CoDeSEG，它在潜在博弈框架内通过最小化网络的二维结构熵来识别社区。节点根据最大化二维结构熵效用函数的策略决定留在当前社区或移动到另一个社区。我们还引入一种基于结构熵的节点重叠启发式方法来检测重叠社区，具有近线性时间复杂度。此外，我们设计了一种基于级联影响传播的自适应社区更新策略，将CoDeSEG有效扩展到动态社区检测场景。在14个大规模网络上的实验结果表明，CoDeSEG在重叠、非重叠、动态这三项社区检测任务中均达到了最优性能，同时在检测效率上也有显著提升。
英文摘要：
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.
