The relationship between objects can be described from different angles. Although multiple kinds of relationships make the connections between objects complex, they bring in more discriminative information for the clustering tasks. Therefore, how to effectively fuse multiple kinds of relationships becomes a critical problem. In this paper, we propose a novel Multi-graph Convolutional Clustering Network which deeply explores the feature information of nodes and fuses the multiple kinds of relationships between nodes. Unlike most graph convolutional clustering methods that only exploit the single graph or directly fuse multiple graphs into a unified graph before the graph convolution operation, we firstly build multiple parallelled graph convolution layers for each graph to learn diverse data representations, which fully exploits different statistics information between graphs. Then, a designed multi-graph attention module fuses above data representations and considers the importance of each graph. Besides, the proposed model completes the transition from single graph to multiple graphs, which reduces the dependence of the quality of the single graph and enhances the robustness to graphs. Experimental results verify that the proposed multi-graph convolution clustering performs better than the traditional single-graph convolution clustering.

The core of multi‐view clustering is how to exploit the shared and specific information of multi‐view data properly. The data missing and incompleteness bring great challenges to multi‐view clustering. In this paper, we propose an innovative multi‐view subspace clustering method with incomplete graph information, so‐called incomplete multiple graphs clustering. Specifically, we creatively separate one shared and multiple specific graphs from multiple raw graph data, and exploit the mask fusion strategy and block diagonal regulariser to obtain the inherent category information. To handle the incomplete multiple graph data, we utilise multiple indicator matrices to mark the missing elements existed in each raw graph. In addition, the weight of each raw graph is adaptively learnt according to the graph importance. The alternative direction optimization algorithm is employed to solve our proposed methods. Finally, we also analyse the algorithm convergence and the computation complexity in detail. The clustering results on six real‐world datasets show that our method obviously outperforms a serious of classic incomplete multi‐view clustering methods.

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