Bayesian Neighborhood Adaptation for Graph Neural Networks

Paribesh Regmi, Rui Li, Kishan K C

Keywords: Graph Neural Network, Bayesian Inference

Abstract: The neighborhood scope (i.e., number of hops) where graph neural networks (GNNs) aggregate information to characterize a node's statistical property is critical to GNNs' performance. Two-stage approaches, training and validating GNNs for every pre-specified neighborhood scope to search for the best setting, is a daunting and time-consuming task and tends to be biased due to the search space design. How to adaptively determine proper neighborhood scopes for the aggregation process for both homophilic and heterophilic graphs remains largely unexplored. We thus propose to model the GNNs' message-passing behavior on a graph as a stochastic process by treating the number of hops as a beta process. This Bayesian framework allows us to infer the most plausible neighborhood scope for messsage aggregation simultaneously with the optimization of GNN parameters. Our theoretical analysis show the scope inference improves the expressivity of GNN models. Experiments on benchmark homophilic and heterophilic datasets show that the proposed method is compatible with state-of-the-art GNN variants, improving their performance and providing well-calibrated predictions.

Primary Area: learning on graphs and other geometries & topologies

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Submission Number: 3881