  The empirical studies of Graph Neural Networks (GNNs) broadly take the
original node feature and adjacency relationship as singleview input, ignoring
the rich information of multiple graph views. To circumvent this issue, the
multiview graph analysis framework has been developed to fuse graph information
across views. How to model and integrate shared (i.e. consistency) and
view-specific (i.e. complementarity) information is a key issue in multiview
graph analysis. In this paper, we propose a novel Multiview Variational Graph
Information Bottleneck (MVGIB) principle to maximize the agreement for common
representations and the disagreement for view-specific representations. Under
this principle, we formulate the common and view-specific information
bottleneck objectives across multiviews by using constraints from mutual
information. However, these objectives are hard to directly optimize since the
mutual information is computationally intractable. To tackle this challenge, we
derive variational lower and upper bounds of mutual information terms, and then
instead optimize variational bounds to find the approximate solutions for the
information objectives. Extensive experiments on graph benchmark datasets
demonstrate the superior effectiveness of the proposed method.
