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.

本研究提出一种名为 MVGIB 的多视角变分图信息瓶颈原则用于跨视角融合图信息，并使用相互信息约束公式化其共享信息和视角特定信息。经过广泛的图形基准数据集实验表明了所提出方法的卓越效果。