Abstract: Structural deep clustering(SDCN) integrates autoencoders and graph convolutional networks(GCN) to jointly leverage sample feature information and graph structural information during clustering.Traditional SDCN primarily relies on distribution-driven implicit optimization of cluster centers,making it sensitive to boundary samples and class imbalance.In particular,when sample distributions are complex,inter-class boundaries are ambiguous,or adjacency relations contain noise,deviations of cluster centers may further amplify pseudo-label errors and weaken the collaborative optimization between the structural branch and the attribute branch.To address this limitation,this work introduces fuzzy C-means(FCM) center refinement combined with a rollback gating mechanism,resulting in an improved structural deep clustering algorithm,denoted as SDCN+FCM.The proposed algorithm improves the quality of center estimation by periodically and explicitly refining cluster centers,and utilizes a KL divergence-based rollback gating mechanism to determine whether to accept the current center update,thereby mitigating harmful updates and enhancing training stability.Experimental results on the USPS,HHAR,REUT,ACM,and DBLP datasets demonstrate that the proposed algorithm outperforms conventional SDCN on the primary evaluation metric across these datasets,while exhibiting improved training stability.

Key words: Deep clustering, Structural deep clustering, Graph convolutional networks, Fuzzy C-means, Rollback gating, Self-supervised learning