Integrating Fuzzy Clustering and Graph Convolution Network to Accurately Identify Clusters From Attributed Graph

作者：Yue Yang, Guodong Li, Dongxu Li, Jun Zhang, Pengwei Hu, Lun Hu · 发表于：IEEE Transactions on Network Science and Engineering · 年份：2024 · DOI：10.1109/tnse.2024.3524077 · 被引用次数：66 · 研究领域：Complex Network Analysis Techniques、Advanced Clustering Algorithms Research、Text and Document Classification Technologies

Attributed graph clustering is of significance for an in-depth understanding of the intrinsic organization of complex networks. Recently, owing to the powerful learning capability of deep neural networks, increasing attention has been paid to developing more advanced deep learning approaches for attributed graph clustering. However, these approaches mainly focus on the purpose of obtaining general node representations, followed by clustering as a downstream task in combination with traditional techniques. Intuitively, the clustering performance is further limited due to the lack of achieving the desirable clustering quality in the graph representation learning process. To this end, this paper proposes a novel end-to-end attributed graph clustering model, namely FCGCN, by integrating fuzzy clustering and graph convolution network. FCGCN is trained toward optimizing an unsupervised fuzzy-based clustering objective, which is specifically formulated for precisely evaluating the clustering quality by considering the fuzzy memberships of nodes over clusters. To avoid the generation of undesirable clusters, we introduce a tailored regularization term by alleviating the over-smoothing issue on graph neural networks. By doing so, an explicit connection between graph representation learning and clustering can thus be established, considerably improving the clustering performance. To evaluate the performance of FCGCN, extensive experiments are conducted on a total of five real attribute...