  Multi-view graph clustering (MGC) methods are increasingly being studied due
to the explosion of multi-view data with graph structural information. The
critical point of MGC is to better utilize the view-specific and view-common
information in features and graphs of multiple views. However, existing works
have an inherent limitation that they are unable to concurrently utilize the
consensus graph information across multiple graphs and the view-specific
feature information. To address this issue, we propose Variational Graph
Generator for Multi-View Graph Clustering (VGMGC). Specifically, a novel
variational graph generator is proposed to extract common information among
multiple graphs. This generator infers a reliable variational consensus graph
based on a priori assumption over multiple graphs. Then a simple yet effective
graph encoder in conjunction with the multi-view clustering objective is
presented to learn the desired graph embeddings for clustering, which embeds
the inferred view-common graph and view-specific graphs together with features.
Finally, theoretical results illustrate the rationality of VGMGC by analyzing
the uncertainty of the inferred consensus graph with information bottleneck
principle. Extensive experiments demonstrate the superior performance of our
VGMGC over SOTAs.
