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Qincheng Lu

2 accepted papers

2023

When Do Graph Neural Networks Help with Node Classification? Investigating the Homophily Principle on Node Distinguishability

NeurIPS 2023poster

Homophily principle, i.e., nodes with the same labels are more likely to be connected, has been believed to be the main reason for the performance superiority of Graph Neural Networks (GNNs) over Neural Networks on node classification tasks. Recent research suggests that, even in the absence of homo…

Cited by 88SourcePDFScholar
2022

Revisiting Heterophily For Graph Neural Networks

NeurIPS 2022accept

Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using graph structures based on the relational inductive bias (homophily assumption). While GNNs have been commonly believed to outperform NNs in real-world tasks, recent work has identified a non-trivial set of datasets where their…