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The empirical studies of Graph Neural Networks (GNNs) broadly take theoriginal node feature and adjacency relationship as singleview input, ignoringthe rich information of multiple graph views. To circumvent this issue, themultiview graph analysis framework has been developed to fuse graph informationacross views. How to model and integrate shared (i.e. consistency) andview-specific (i.e. complementarity) information is a key issue in multiviewgraph analysis. In this paper, we propose a novel Multiview Variational GraphInformation Bottleneck (MVGIB) principle to maximize the agreement for commonrepresentations and the disagreement for view-specific representations. Underthis principle, we formulate the common and view-specific informationbottleneck objectives across multiviews by using constraints from mutualinformation. However, these objectives are hard to directly optimize since themutual information is computationally intractable. To tackle this challenge, wederive variational lower and upper bounds of mutual information terms, and theninstead optimize variational bounds to find the approximate solutions for theinformation objectives. Extensive experiments on graph benchmark datasetsdemonstrate the superior effectiveness of the proposed method.

Towards Consistency and Complementarity: A Multiview Graph Information Bottleneck Approach