Web: http://arxiv.org/abs/2205.15117

Sept. 16, 2022, 1:13 a.m. | Yangze Zhou, Gitta Kutyniok, Bruno Ribeiro

stat.ML updates on arXiv.org arxiv.org

This work provides the first theoretical study on the ability of graph
Message Passing Neural Networks (gMPNNs) -- such as Graph Neural Networks
(GNNs) -- to achieve counterfactually-invariant representations for inductive
out-of-distribution (OOD) link prediction tasks, where deployment (test) graph
sizes are larger than training graphs. We first prove non-asymptotic bounds
showing that link predictors based on permutation-equivariant (structural) node
embeddings obtained by gMPNNs can converge to a random guess as test graphs get
larger. We then propose a theoretically-sound …

arxiv gnns graphs link prediction prediction test

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