Web: http://arxiv.org/abs/2111.07337

June 24, 2022, 1:11 a.m. | Guoji Fu, Peilin Zhao, Yatao Bian

stat.ML updates on arXiv.org arxiv.org

Graph neural networks (GNNs) have demonstrated superior performance for
semi-supervised node classification on graphs, as a result of their ability to
exploit node features and topological information simultaneously. However, most
GNNs implicitly assume that the labels of nodes and their neighbors in a graph
are the same or consistent, which does not hold in heterophilic graphs, where
the labels of linked nodes are likely to differ. Hence, when the topology is
non-informative for label prediction, ordinary GNNs may work significantly …

arxiv graph graph neural networks lg networks neural neural networks

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