Abstract Incomplete Multi-View Clustering (IMVC) attempts to give an optimal clustering solution for incomplete multi-view data that suffer from missing instances in certain views. However, most existing IMVC methods still have various drawbacks in practical applications, such as arbitrary incomplete scenarios cannot be handled; the computational cost is relatively high; most valuable nonlinear relations among samples are often ignored; complementary information among views is not sufficiently exploited. To address the above issues, in this paper, we present a novel and flexible unified graph learning framework, called Multiple Kernel-based Anchor Graph coupled low-rank Tensor learning for Incomplete Multi-View Clustering (MKAGT_IMVC), whose goal is to adaptively learn the optimal unified similarity matrix from all incomplete views. Specifically, according to the characteristics of incomplete multi-view data, MKAGT_IMVC innovatively improves an anchor selection strategy. Then, a novel cross-view anchor graph fusion mechanism is introduced to construct multiple fused complete anchor graphs, which captures more the intra-view and inter-view nonlinear relations. Moreover, a graph learning model combining low-rank tensor constraint and consensus graph constraint is developed, where all fused complete anchor graphs are regarded as prior knowledge to initialize this model. Extensive experiments conducted on eight incomplete multi-view datasets clearly show that our method delivers superior performance relative to some state-of-the-art methods in terms of clustering ability and time-consuming.

Summary The information propagation mechanism in graph‐structured networks such as social networks is the foundation of network security. The graph convolutional network (GCN) is a powerful approach for semisupervised node classification on graph‐structure data. The vertex features which pass through the graph network are affected by the k‐hop neighborhood vertices. However, current high‐order GCN approaches merged the k‐hop neighborhood using coarse pooling and complicated weight parameters. To reduce the computational complexity and preserve topological of the graph data, with weight sharing mechanism we propose a novel GCN based on a novel higher‐order pooling layer for semisupervised classification. The proposed model and its variants are experimental studied on several large‐scale citation network datasets using semisupervised learning. The experimental results show that the proposed model and its variants have lower computational complexity and achieve the state‐of‐the‐art in the node classification accuracy.