Affinity-Aware Graph Networks

Ameya Velingker, Ali Kemal Sinop, Ira Ktena, Petar Veličković, Sreenivas Gollapudi

Keywords: Graph neural networks, message passing networks, effective resistance

TL;DR: We show how to use affinity measures arising from random walks (e.g., effective resistance) to design message passing networks that are shown to outperform various benchmarks with fewer message passing steps.

Abstract: Graph Neural Networks (GNNs) have emerged as a powerful technique for learning on relational data. Owing to the relatively limited number of message passing steps they perform—and hence a smaller receptive field—there has been significant interest in improving their expressivity by incorporating structural aspects of the underlying graph. In this paper, we explore the use of affinity measures as features in graph neural networks, in particular measures arising from random walks, including effective resistance, hitting and commute times. We propose message passing networks based on these features and evaluate their performance on a variety of node and graph property prediction tasks.

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