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Nowadays, Graph Neural Networks (GNNs) following the Message Passing paradigmbecome the dominant way to learn on graphic data. Models in this paradigm haveto spend extra space to look up adjacent nodes with adjacency matrices andextra time to aggregate multiple messages from adjacent nodes. To address thisissue, we develop a method called LinkDist that distils self-knowledge fromconnected node pairs into a Multi-Layer Perceptron (MLP) without the need toaggregate messages. Experiment with 8 real-world datasets shows the MLP derivedfrom LinkDist can predict the label of a node without knowing its adjacenciesbut achieve comparable accuracy against GNNs in the contexts of semi- andfull-supervised node classification. Moreover, LinkDist benefits from itsNon-Message Passing paradigm that we can also distil self-knowledge fromarbitrarily sampled node pairs in a contrastive way to further boost theperformance of LinkDist.

Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages