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.

翻译：社区检测旨在识别网络内部紧密连接而组间连接稀疏的节点簇，对于分析现实世界系统的网络结构与功能至关重要。现有基于图卷积网络（GCN）的社区检测方法大多聚焦于节点级信息，而忽视了社区级特征，导致其在大规模网络上的性能受限。为解决这一问题，我们提出LQ-GCN——一种从局部社区视角出发的重叠社区检测模型。LQ-GCN采用伯努利-泊松模型构建社区隶属矩阵，形成端到端的检测框架。通过以局部模块度为目标函数，该模型融合局部社区信息以提升聚类结果的质量与准确性。此外，我们对传统GCN架构进行了优化，以增强模型在大规模网络中识别重叠社区的能力。实验结果表明，在多个现实世界基准数据集上，LQ-GCN相较于基线模型在标准化互信息（NMI）指标上最高提升33%，在召回率指标上提升26.3%。