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DropEdge: Towards Deep Graph Convolutional Networks on Node Classification

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arxiv 1907.10903 v4 pith:TFQJK77C submitted 2019-07-25 cs.LG cs.NIstat.ML

Yu Rong , Wenbing Huang , Tingyang Xu , Junzhou Huang This is my paper
classification cs.LGcs.NIstat.ML
keywords dropedgeover-smoothingdeepgraphclassificationconvolutionalemphgcns
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\emph{Over-fitting} and \emph{over-smoothing} are two main obstacles of developing deep Graph Convolutional Networks (GCNs) for node classification. In particular, over-fitting weakens the generalization ability on small dataset, while over-smoothing impedes model training by isolating output representations from the input features with the increase in network depth. This paper proposes DropEdge, a novel and flexible technique to alleviate both issues. At its core, DropEdge randomly removes a certain number of edges from the input graph at each training epoch, acting like a data augmenter and also a message passing reducer. Furthermore, we theoretically demonstrate that DropEdge either reduces the convergence speed of over-smoothing or relieves the information loss caused by it. More importantly, our DropEdge is a general skill that can be equipped with many other backbone models (e.g. GCN, ResGCN, GraphSAGE, and JKNet) for enhanced performance. Extensive experiments on several benchmarks verify that DropEdge consistently improves the performance on a variety of both shallow and deep GCNs. The effect of DropEdge on preventing over-smoothing is empirically visualized and validated as well. Codes are released on~\url{https://github.com/DropEdge/DropEdge}.

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Cited by 11 Pith papers

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

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    A 7M-parameter graph ECG foundation model pre-trained with random lead dropout matches 12-lead models on full input and beats zero-padded baselines on most datasets with 1-2 leads.

  2. Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark

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    BeGIN provides 10 graph datasets with six label-noise types and a broad evaluation, showing that LLM-simulated noise hurts GNNs more than uniform or pairwise noise.

  3. Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization

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    PrunE prunes spurious edges with a size constraint and an epsilon-probability alignment, reporting state-of-the-art graph OOD results despite a weak theoretical analysis.

  4. Khan-GCL: Kolmogorov-Arnold Network Based Graph Contrastive Learning with Hard Negatives

    cs.LG 2025-05 conditional novelty 6.0 of 10

    Khan-GCL combines KAN encoders with coefficient-based critical feature identification to generate hard negatives and reports state-of-the-art graph classification results.

  5. 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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    A moment-based clustering of graphs into latent graphon mixtures improves graph mixup and contrastive learning, with a claimed tighter motif-density concentration bound.

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    CAMP updates nodes in centrality-ranked batches to spread information across GNN layers and claims to reduce oversquashing without rewiring, but the proof and evidence are not convincing.

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    A training strategy that grows GCN depth gradually, using LoRA fine-tuning and identity initialization, is claimed to reduce over-smoothing and improve accuracy of deep vanilla GCNs.

  9. SGS-GNN: A Supervised Graph Sparsification method for Graph Neural Networks

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    EdgeGAT, combining EdgeConv and graph attention with PCA features, reports mIoU of 93.20% on the Pheno4D maize dataset and 73.35% on the Ao dataset for leaf, stem, and soil segmentation.

  11. Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization

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    A single-author preprint re-frames GNN over-smoothing as Anderson localization, defining a participation-degree metric and proposing degree-dependent edge reweighting as mitigation, without proof or experiments.

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