mlsanityData to 7 Oct 2026

NodeMixup: Tackling Under-Reaching for Graph Neural Networks

arXiv 2312.13032 ↗First posted 20 Dec 2023cs.LG cs.AIcited by 12

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Abstract (arXiv)

Graph Neural Networks (GNNs) have become mainstream methods for solving the semi-supervised node classification problem. However, due to the uneven location distribution of labeled nodes in the graph, labeled nodes are only accessible to a small portion of unlabeled nodes, leading to the under-reaching issue. In this study, we firstly reveal under-reaching by conducting an empirical investigation on various well-known graphs. Then, we demonstrate that under-reaching results in unsatisfactory distribution alignment between labeled and unlabeled nodes through systematic experimental analysis, significantly degrading GNNs' performance. To tackle under-reaching for GNNs, we propose an architecture-agnostic method dubbed NodeMixup. The fundamental idea is to (1) increase the reachability of labeled nodes by labeled-unlabeled pairs mixup, (2) leverage graph structures via fusing the neighbor connections of intra-class node pairs to improve performance gains of mixup, and (3) use neighbor label distribution similarity incorporating node degrees to determine sampling weights for node mixup. Extensive experiments demonstrate the efficacy of NodeMixup in assisting GNNs in handling under-reaching. The source code is available at urlhttps://github.com/WeigangLu/NodeMixup.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 28 of its 40 references that are in the map. First posted on arXiv 20 Dec 2023.

Builds on

28 of 40 references.

References · 28

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Graph Attention Networkscited by 8,854
  4. 2017Inductive Representation Learning on Large Graphscited by 6,284
  5. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  6. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  7. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  8. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  9. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  10. 2019Similarity of Neural Network Representations Revisitedcited by 1,695
  11. 2019Simplifying Graph Convolutional Networkscited by 1,470
  12. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,390
16 more
  1. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
  2. 2018Pitfalls of Graph Neural Network Evaluationcited by 994
  3. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 987
  4. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 970
  5. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 875
  6. 2020Simple and Deep Graph Convolutional Networkscited by 857
  7. 2020Adaptive Universal Generalized PageRank Graph Neural Networkcited by 592
  8. 2021Understanding over-squashing and bottlenecks on graphs via curvaturecited by 471
  9. 2021In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learningcited by 301
  10. 2023On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topologycited by 163
  11. 2021Topology-Imbalance Learning for Semi-Supervised Node Classificationcited by 56
  12. 2021GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Predictioncited by 36
  13. 2022Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashingcited by 31
  14. 2021Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservationcited by 30
  15. 2022GraphMAD: Graph Mixup for Data Augmentation using Data-Driven Convex Clusteringcited by 13
  16. 2022Metric Based Few-Shot Graph Classificationcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 7; 2026: 2.

12 papers.

  1. 2025Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Predictioncited by 13
  2. 2024AdaGMLP: AdaBoosting GNN-to-MLP Knowledge Distillationcited by 6
  3. 2024IntraMix: Intra-Class Mixup Generation for Accurate Labels and Neighborscited by 4
  4. 2025Discrepancy-Aware Graph Mask Auto-Encodercited by 4
  5. 2025On the under-reaching phenomenon in message-passing neural PDE solvers: revisiting the CFL conditioncited by 4
  6. 2025Backward Oversmoothing: why is it hard to train deep Graph Neural Networks?cited by 2
  7. 2025ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offscited by 1
  8. 2025VDW-GNNs: Vector diffusion wavelets for geometric graph neural networkscited by 1
  9. 2026Traceable Latent Variable Discovery Based on Multi-Agent Collaborationcited by 1
  10. 2026MDGMIX: Boundary-Aware Subgraph Mixing for Multi-Domain Graph Pre-Trainingcited by 1
  11. 2024Dual-perspective Cross Contrastive Learning in Graph Transformerscited by 0
  12. 2025Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methodscited by 0