Contrastive and attentive graph learning for multi-view clustering

Graph-based multi-view clustering aims to take advantage of multiple view graph information to provide clustering solutions. The consistency constraint of multiple views is the key of multi-view graph clustering. Most existing studies generate fusion graphs and constrain multi-view consistency by clustering loss. We argue that local pair-view consistency can achieve fine-modeling of consensus information in multiple views. Towards this end, we propose a novel Contrastive and Attentive Graph Learning framework for multi-view clustering (CAGL). Specifically, we design a contrastive fine-modeling in multi-view graph learning using maximizing the similarity of pair-view to guarantee the consistency of multiple views. Meanwhile, an Att-weighted refined fusion graph module based on attention networks to capture the capacity difference of different views dynamically and further facilitate the mutual reinforcement of single view and fusion view. Besides, our CAGL can learn a specialized representation for clustering via a self-training clustering module. Finally, we develop a joint optimization objective to balance every module and iteratively optimize the proposed CAGL in the framework of graph encoder–decoder. Experimental results on six benchmarks across different modalities and sizes demonstrate that our CAGL outperforms state-of-the-art baselines.