从局部视角出发，提升重叠社区检测的准确率
A Local Perspective-based Model for Overlapping Community Detection
- 基于局部模块度构建社区隶属矩阵，融合社区级特征
- 在多个数据集上NMI提升33%，召回率提高26.3%
- 适合大规模网络中识别重叠社区的场景
社区检测旨在识别网络中节点高度连接的子群，是分析真实系统结构与功能的关键。现有基于GCN的方法多关注节点级信息，忽略社区级特征，导致在大规模网络上表现受限。为此，本文提出LQ-GCN模型，从局部社区视角出发，采用伯努利-泊松模型构建社区隶属矩阵，建立端到端检测框架。通过以局部模块度为优化目标，融入局部社区信息，提升聚类质量与准确性。同时优化传统GCN架构，增强对大规模网络中重叠社区的识别能力。实验表明，LQ-GCN在多个真实基准数据集上，相较基线模型，归一化互信息（NMI）最高提升33%，召回率最高提升26.3%。
原文摘要 · Abstract (English)
Community detection, which identifies densely connected node clusters with sparse between-group links, is vital for analyzing network structure and function in real-world systems. Most existing community detection methods based on GCNs primarily focus on node-level information while overlooking community-level features, leading to performance limitations on large-scale networks. To address this issue, we propose LQ-GCN, an overlapping community detection model from a local community perspective. LQ-GCN employs a Bernoulli-Poisson model to construct a community affiliation matrix and form an end-to-end detection framework. By adopting local modularity as the objective function, the model incorporates local community information to enhance the quality and accuracy of clustering results. Additionally, the conventional GCNs architecture is optimized to improve the model capability in identifying overlapping communities in large-scale networks. Experimental results demonstrate that LQ-GCN achieves up to a 33% improvement in Normalized Mutual Information (NMI) and a 26.3% improvement in Recall compared to baseline models across multiple real-world benchmark datasets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。