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PairNorm: Tackling Oversmoothing in GNNs

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arxiv 1909.12223 v2 pith:M2UYY7YC submitted 2019-09-26 cs.LG stat.ML

Lingxiao Zhao , Leman Akoglu This is my paper · ORCID
classification cs.LGstat.ML
keywords pairnormoversmoothinggnnsgraphdeeperembeddingsnodeperformance
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The performance of graph neural nets (GNNs) is known to gradually decrease with increasing number of layers. This decay is partly attributed to oversmoothing, where repeated graph convolutions eventually make node embeddings indistinguishable. We take a closer look at two different interpretations, aiming to quantify oversmoothing. Our main contribution is PairNorm, a novel normalization layer that is based on a careful analysis of the graph convolution operator, which prevents all node embeddings from becoming too similar. What is more, PairNorm is fast, easy to implement without any change to network architecture nor any additional parameters, and is broadly applicable to any GNN. Experiments on real-world graphs demonstrate that PairNorm makes deeper GCN, GAT, and SGC models more robust against oversmoothing, and significantly boosts performance for a new problem setting that benefits from deeper GNNs. Code is available at https://github.com/LingxiaoShawn/PairNorm.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Dynamic Triangulation-Based Graph Rewiring for Graph Neural Networks

    cs.LG 2025-08 conditional novelty 6.0 of 10

    A learned triangle-selection module rewires graphs for GNNs, improving node classification over prior rewiring methods on 9 of 10 benchmarks.

  2. Resolving Oversmoothing with Opinion Dissensus

    cs.LG 2025-01 conditional novelty 6.0 of 10

    BIMP, a continuous-depth GNN based on nonlinear opinion dynamics, is shown to avoid oversmoothing when its constant input has unique entries, and it outperforms baselines on ten node-classification datasets.

  3. From Diffusion to Reaction-Diffusion: A Dynamical-Systems View of Oversmoothing in Hypergraph Neural Networks

    cs.LG 2026-07 conditional novelty 5.0 of 10

    Hypergraph diffusion provably collapses node representations, and a reaction term that exactly cancels diffusion dissipation keeps a designed transverse energy level nonzero in Hypergraph Neural Reaction–Diffusion (HNRD).

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