AGCN: A Service Computing-Oriented Anchor-Guided Graph Clustering Network

Jieren Cheng, Faqiang Zeng, Jimei Li, Ling Liu, Neal Xiong, Xin Peng

Abstract: Deep graph clustering aims to uncover latent structural patterns within user behavior graphs to effectively group users based on behavioral similarity, thereby enhancing the efficiency of personalized service recommendation. However, most existing generative clustering methods heavily rely on encoder–decoder architectures and their pretraining procedures, which not only increase training complexity but also result in insufficient robustness and unstable optimization. To address these limitations, this paper proposes an Anchor-Guided Graph Clustering Network (AGCN) that eliminates the need for pretraining. The proposed method introduces an anchor-guided learning strategy that automatically selects high-confidence anchor samples farthest from the distribution boundary and utilizes the k-means++ algorithm to generate pseudo-labels in the diffusion feature space. Furthermore, AGCN integrates an anchor expansion strategy and a commonality enhancement mechanism to significantly improve clustering discriminability and recommendation accuracy. Theoretical analysis and extensive experiments on multiple real-world datasets demonstrate that the proposed method outperforms state-of-the-art approaches in terms of both clustering accuracy and stability.

External IDs:doi:10.1109/tsc.2025.3640664