Affinity-Aware Graph Networks. (arXiv:2206.11941v1 [cs.LG])

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 …

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