Online Multi-Relational Clustering with Dominant View Mining - Zhu, Zhengzhong; Zhou, Pei; Wang, Dongsheng; Cheng, Li; Zhu, Jiangping (Proceedings of the AAAI Conference on Artificial Intelligence, 2026)
Multi-relational graph clustering aims to uncover complex node interactions by leveraging multiple relational views, yet existing methods often suffer from two key limitations: they assume equal importance across views and decouple representation learning from clustering, both of which hinder overall performance. To address these issues, we propose OMC-DVM, a novel end-to-end Online Multi-Relational Graph Clustering With Dominant View Mining framework. OMC-DVM introduces two core innovations: (1) A unsupervised dominant view mining module that dynamically identifies the dominant view using Maximum Mean Discrepancy (MMD) and adaptively aligns other views to it, mitigating view imbalance. (2) An online ,multi-relational clustering process that unifies representation learning and clustering into a single stage. By performing clustering-level contrastive learning , OMC-DVM directly generates cluster assignments in an end-to-end manner. Extensive experiments on both real-world and synthetic benchmark datasets demonstrate that OMC-DVM not only achieves state-of-the-art clustering performance but also effectively alleviates the view imbalance problem in multi-relational graphs.