mlsanityData to 7 Oct 2026

Non-Local Graph Neural Networks

arXiv 2005.14612 ↗First posted 29 May 2020cs.LG stat.MLcited by 91

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Abstract (arXiv)

Modern graph neural networks (GNNs) learn node embeddings through multilayer local aggregation and achieve great success in applications on assortative graphs. However, tasks on disassortative graphs usually require non-local aggregation. In addition, we find that local aggregation is even harmful for some disassortative graphs. In this work, we propose a simple yet effective non-local aggregation framework with an efficient attention-guided sorting for GNNs. Based on it, we develop various non-local GNNs. We perform thorough experiments to analyze disassortative graph datasets and evaluate our non-local GNNs. Experimental results demonstrate that our non-local GNNs significantly outperform previous state-of-the-art methods on seven benchmark datasets of disassortative graphs, in terms of both model performance and efficiency.

Key facts. Cited by 91 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 May 2020.

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Citing papers per year: 2021: 21; 2022: 15; 2023: 22; 2024: 21; 2025: 8; 2026: 4.

91 papers.

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  1. 2021Beyond Low-frequency Information in Graph Convolutional Networkscited by 361
  2. 2021Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodscited by 309
  3. 2021Self-Supervised Learning of Graph Neural Networks: A Unified Reviewcited by 184
  4. 2021Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networkscited by 182
  5. 2022Finding Global Homophily in Graph Neural Networks When Meeting Heterophilycited by 179
  6. 2022Graph Neural Networks for Graphs with Heterophily: A Surveycited by 175
  7. 2022Revisiting Heterophily For Graph Neural Networkscited by 169
  8. 2021Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patternscited by 95
  9. 2021Parameter Prediction for Unseen Deep Architecturescited by 89
  10. 2021GraphEBM: Molecular Graph Generation with Energy-Based Modelscited by 85
  11. 2021Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?cited by 82
  12. 2021New Benchmarks for Learning on Non-Homophilous Graphscited by 81
38 more
  1. 2022A Survey on Graph Representation Learning Methodscited by 66
  2. 2021Universal Approximation of Functions on Setscited by 62
  3. 2021Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoodscited by 57
  4. 2022A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networkscited by 49
  5. 2023Ewald-based Long-Range Message Passing for Molecular Graphscited by 46
  6. 2022Graph Auto-Encoder Via Neighborhood Wasserstein Reconstructioncited by 28
  7. 2021Graph Pointer Neural Networkscited by 27
  8. 2023Probabilistically Rewired Message-Passing Neural Networkscited by 26
  9. 2022HL-Net: Heterophily Learning Network for Scene Graph Generationcited by 22
  10. 2021How does Heterophily Impact the Robustness of Graph Neural Networks? Theoretical Connections and Practical Implicationscited by 21
  11. 2024Partitioning Message Passing for Graph Fraud Detectioncited by 21
  12. 2022Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Neural Networkscited by 19
  13. 2021Beyond Low-Pass Filters: Adaptive Feature Propagation on Graphscited by 18
  14. 2022Simplified Graph Convolution with Heterophilycited by 17
  15. 2022Node-oriented Spectral Filtering for Graph Neural Networkscited by 17
  16. 2022Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with Heterophilycited by 15
  17. 2023Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learningcited by 15
  18. 2024Understanding Heterophily for Graph Neural Networkscited by 15
  19. 2024Probabilistic Graph Rewiring via Virtual Nodescited by 15
  20. 2023ZeroI2V: Zero-Cost Adaptation of Pre-trained Transformers from Image to Videocited by 14
  21. 2022ES-GNN: Generalizing Graph Neural Networks Beyond Homophily with Edge Splittingcited by 13
  22. 2023MUSE: Multi-View Contrastive Learning for Heterophilic Graphscited by 13
  23. 2024A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directionscited by 13
  24. 2021WGCN: Graph Convolutional Networks with Weighted Structural Featurescited by 12
  25. 2022From Local to Global: Spectral-Inspired Graph Neural Networkscited by 11
  26. 2023SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregationcited by 10
  27. 2023Graph Propagation Transformer for Graph Representation Learningcited by 9
  28. 2023GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropycited by 9
  29. 2024LOGIN: A Large Language Model Consulted Graph Neural Network Training Frameworkcited by 8
  30. 2023Neighborhood Homophily-based Graph Convolutional Networkcited by 7
  31. 2023Heterophily-Aware Graph Attention Networkcited by 7
  32. 2023Finding Heterophilic Neighbors via Confidence-based Subgraph Matching for Semi-supervised Node Classificationcited by 7
  33. 2023Spatial Heterophily Aware Graph Neural Networkscited by 7
  34. 2024Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Trainingcited by 7
  35. 2021FDGATII : Fast Dynamic Graph Attention with Initial Residual and Identity Mappingcited by 6
  36. 2021Deformable Graph Convolutional Networkscited by 6
  37. 2024Representation Learning on Heterophilic Graph with Directional Neighborhood Attentioncited by 6
  38. 2024AGS-GNN: Attribute-guided Sampling for Graph Neural Networkscited by 6

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