Tang Feiyi, Li Junxian, Liu Xi, Chang Chao, Teng Luyao
School of Information Engineering, Guangzhou Panyu Polytechnic, Guangzhou, 511483, China.
Sci Rep. 2025 Jan 31;15(1):3952. doi: 10.1038/s41598-024-84962-4.
Community detection in graph networks is a fundamental yet challenging task due to limitations in existing methods. Traditional Graph Convolutional Network (GCN)-based models often suffer from over-smoothing, which results in indistinguishable node representations after excessive aggregation. Additionally, many models face computational inefficiency, restricting their scalability on large-scale networks. To address these challenges, we propose GATFELPA, a hybrid community detection model that combines a Graph Attention Network (GAT) with an enhanced label propagation algorithm (DPCELPA). GATFELPA employs an adaptive strategy to dynamically determine the optimal number of aggregation layers, effectively mitigating over-smoothing by balancing intra-community compactness and inter-community separability. A novel similarity preservation module further enhances the model's ability to differentiate communities by retaining local and global dissimilarities in heterogeneous networks. Comparative experiments on four real-world datasets, including large-scale networks like ogbn-arxiv, demonstrate that GATFELPA achieves superior performance across most metrics, particularly excelling in accuracy, robustness, and scalability. These results highlight GATFELPA as a promising approach for addressing complex community detection tasks.
由于现有方法存在局限性，图网络中的社区检测是一项基本但具有挑战性的任务。传统的基于图卷积网络（GCN）的模型经常遭受过度平滑的问题，这导致在过度聚合后节点表示难以区分。此外，许多模型面临计算效率低下的问题，限制了它们在大规模网络上的可扩展性。为了应对这些挑战，我们提出了GATFELPA，一种将图注意力网络（GAT）与增强标签传播算法（DPCELPA）相结合的混合社区检测模型。GATFELPA采用自适应策略动态确定聚合层的最佳数量，通过平衡社区内紧凑性和社区间可分离性有效缓解过度平滑问题。一个新颖的相似性保留模块通过保留异构网络中的局部和全局差异进一步增强了模型区分社区的能力。在包括ogbn-arxiv等大规模网络在内的四个真实世界数据集上的对比实验表明，GATFELPA在大多数指标上取得了卓越的性能，尤其在准确性、鲁棒性和可扩展性方面表现出色。这些结果突出了GATFELPA作为解决复杂社区检测任务的一种有前途的方法。
