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Locality-Aware Graph-Rewiring in GNNs
May 7, 2024, 4:44 a.m. | Federico Barbero, Ameya Velingker, Amin Saberi, Michael Bronstein, Francesco Di Giovanni
cs.LG updates on arXiv.org arxiv.org
Abstract: Graph Neural Networks (GNNs) are popular models for machine learning on graphs that typically follow the message-passing paradigm, whereby the feature of a node is updated recursively upon aggregating information over its neighbors. While exchanging messages over the input graph endows GNNs with a strong inductive bias, it can also make GNNs susceptible to over-squashing, thereby preventing them from capturing long-range interactions in the given graph. To rectify this issue, graph rewiring techniques have been …
abstract arxiv bias cs.lg feature gnns graph graph neural networks graphs inductive information machine machine learning messages neighbors networks neural networks node paradigm popular type while
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