用新损失函数融合拓扑与属性相似性，提升社区发现效果
Advancing Community Detection with Graph Convolutional Neural Networks: Bridging Topological and Attributive Cohesion
- 设计新损失函数，结合Leiden算法优化全局模块度
- 改进人工标签社区，确保节点连通性且保持标签一致性
- 在多个基准数据集上优于现有顶尖算法
社区检测是现实应用中的关键技术，通过挖掘社交网络中节点的拓扑与属性相似性来发现紧密关联的群体。然而，现有图卷积网络（GCN）在最大化模块度训练时常收敛至次优解。直接使用人工标注社区进行训练会因仅依赖属性而破坏拓扑连通性。为此，本文提出一种基于拓扑与属性相似性的新方法（TAS-Com）。该方法引入新型损失函数，利用高效可扩展的Leiden算法获取具有全局最优模块度的社区结构，并进一步用Leiden算法优化人工标注社区，确保每个社区内部连通。这使得TAS-Com能够在模块度与人类标签符合度之间取得理想平衡。多组基准网络实验表明，TAS-Com显著优于多种先进算法。
原文摘要 · Abstract (English)
Community detection, a vital technology for real-world applications, uncovers cohesive node groups (communities) by leveraging both topological and attribute similarities in social networks. However, existing Graph Convolutional Networks (GCNs) trained to maximize modularity often converge to suboptimal solutions. Additionally, directly using human-labeled communities for training can undermine topological cohesiveness by grouping disconnected nodes based solely on node attributes. We address these issues by proposing a novel Topological and Attributive Similarity-based Community detection (TAS-Com) method. TAS-Com introduces a novel loss function that exploits the highly effective and scalable Leiden algorithm to detect community structures with global optimal modularity. Leiden is further utilized to refine human-labeled communities to ensure connectivity within each community, enabling TAS-Com to detect community structures with desirable trade-offs between modularity and compliance with human labels. Experimental results on multiple benchmark networks confirm that TAS-Com can significantly outperform several state-of-the-art algorithms.
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