Jan. 1, 2023, midnight | Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller

JMLR www.jmlr.org

Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks such as node classification and link prediction. However, important unsupervised problems on graphs, such as graph clustering, have proved more resistant to advances in GNNs. Graph clustering has the same overall goal as node pooling in GNNs—does this mean that GNN pooling methods do a good job at clustering graphs? Surprisingly, the answer is no—current GNN pooling methods often fail to recover the cluster structure in cases …

analysis art classification clustering gnns graph graph neural networks graphs link prediction mean networks neural networks node pooling prediction state unsupervised

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