mlsanityData to 7 Oct 2026

Cooperative Graph Neural Networks

arXiv 2310.01267 ↗First posted 2 Oct 2023cs.LG cs.AIcited by 35

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Abstract (arXiv)

Graph neural networks are popular architectures for graph machine learning, based on iterative computation of node representations of an input graph through a series of invariant transformations. A large class of graph neural networks follow a standard message-passing paradigm: at every layer, each node state is updated based on an aggregate of messages from its neighborhood. In this work, we propose a novel framework for training graph neural networks, where every node is viewed as a player that can choose to either 'listen', 'broadcast', 'listen and broadcast', or to 'isolate'. The standard message propagation scheme can then be viewed as a special case of this framework where every node 'listens and broadcasts' to all neighbors. Our approach offers a more flexible and dynamic message-passing paradigm, where each node can determine its own strategy based on their state, effectively exploring the graph topology while learning. We provide a theoretical analysis of the new message-passing scheme which is further supported by an extensive empirical analysis on a synthetic dataset and on real-world datasets.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). Builds on 60 of its 74 references that are in the map. First posted on arXiv 2 Oct 2023.

Builds on

60 of 74 references.

References · 60

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2018How Powerful are Graph Neural Networks?cited by 4,355
  5. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  6. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  7. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,708
  8. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  9. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  10. 2016The Concrete Distribution: A Continuous Relaxation of Discrete Random Variablescited by 1,135
  11. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
  12. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 970
38 more
  1. 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 878
  2. 2020Simple and Deep Graph Convolutional Networkscited by 857
  3. 2020Geom-GCN: Geometric Graph Convolutional Networkscited by 821
  4. 2015Gated Graph Sequence Neural Networkscited by 792
  5. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 786
  6. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
  7. 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 718
  8. 2020A Generalization of Transformer Networks to Graphscited by 653
  9. 2020On the Bottleneck of Graph Neural Networks and its Practical Implicationscited by 624
  10. 2019Graph Transformer Networkscited by 520
  11. 2020Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classificationcited by 500
  12. 2021Understanding over-squashing and bottlenecks on graphs via curvaturecited by 471
  13. 2021Rethinking Graph Transformers with Spectral Attentioncited by 468
  14. 2019Diffusion Improves Graph Learningcited by 414
  15. 2019Provably Powerful Graph Networkscited by 385
  16. 2017Residual Gated Graph ConvNetscited by 341
  17. 2023A critical look at the evaluation of GNNs under heterophily: Are we really making progress?cited by 332
  18. 2020Improving Graph Neural Network Expressivity via Subgraph Isomorphism Countingcited by 312
  19. 2017Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphscited by 307
  20. 2019PairNorm: Tackling Oversmoothing in GNNscited by 297
  21. 2022Long Range Graph Benchmarkcited by 258
  22. 2019A Fair Comparison of Graph Neural Networks for Graph Classificationcited by 244
  23. 2018Modeling polypharmacy side effects with graph convolutional networkscited by 237
  24. 2021Weisfeiler and Lehman Go Cellular: CW Networkscited by 218
  25. 2022Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNscited by 208
  26. 2021Identity-aware Graph Neural Networkscited by 197
  27. 2019What graph neural networks cannot learn: depth vs widthcited by 195
  28. 2020The Surprising Power of Graph Neural Networks with Random Node Initializationcited by 190
  29. 2020Random Features Strengthen Graph Neural Networkscited by 184
  30. 2019Universal Invariant and Equivariant Graph Neural Networkscited by 182
  31. 2023Exphormer: Sparse Transformers for Graphscited by 166
  32. 2023On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topologycited by 163
  33. 2021Equivariant Subgraph Aggregation Networkscited by 154
  34. 2023Graph Inductive Biases in Transformers without Message Passingcited by 150
  35. 2021DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networkscited by 117
  36. 2022FoSR: First-order spectral rewiring for addressing oversquashing in GNNscited by 104
  37. 2022A Generalization of ViT/MLP-Mixer to Graphscited by 93
  38. 2023Where Did the Gap Go? Reassessing the Long-Range Graph Benchmarkcited by 83

50 most cited of 60 All 60 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 9; 2025: 20; 2026: 5.

35 papers.

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  1. 2024Graph Mamba: Towards Learning on Graphs with State Space Modelscited by 84
  2. 2025On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learningcited by 42
  3. 2025Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellencecited by 24
  4. 2023Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreachingcited by 23
  5. 2024Weisfeiler-Leman at the margin: When more expressivity matterscited by 13
  6. 2025Return of ChebNet: Understanding and Improving an Overlooked GNN on Long Range Taskscited by 12
  7. 2025Bringing Graphs to the Table: Zero-shot Node Classification via Tabular Foundation Modelscited by 12
  8. 2024PANDA: Expanded Width-Aware Message Passing Beyond Rewiringcited by 10
  9. 2025Cooperative Sheaf Neural Networkscited by 10
  10. 2024What Is Missing In Homophily? Disentangling Graph Homophily For Graph Neural Networkscited by 9
  11. 2025Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modelingcited by 9
  12. 2025On Measuring Long-Range Interactions in Graph Neural Networkscited by 9
23 more
  1. 2025Depth-Adaptive Graph Neural Networks via Learnable Bakry-'Emery Curvaturecited by 8
  2. 2025Flock: A Knowledge Graph Foundation Model via Learning on Random Walkscited by 8
  3. 2025Mitigating Over-Squashing in Graph Neural Networks by Spectrum-Preserving Sparsificationcited by 7
  4. 2024Preventing Representational Rank Collapse in MPNNs by Splitting the Computational Graphcited by 6
  5. 2025Improving the Effective Receptive Field of Message-Passing Neural Networkscited by 6
  6. 2025PIORF: Physics-Informed Ollivier-Ricci Flow for Long-Range Interactions in Mesh Graph Neural Networkscited by 5
  7. 2025Actions Speak Louder than Prompts: A Large-Scale Study of LLMs for Graph Inferencecited by 4
  8. 2024Improving Graph Neural Networks by Learning Continuous Edge Directionscited by 3
  9. 2024Making Classic GNNs Strong Baselines Across Varying Homophily: A Smoothness-Generalization Perspectivecited by 2
  10. 2025Efficient Learning on Large Graphs using a Densifying Regularity Lemmacited by 2
  11. 2025Early-Exit Graph Neural Networkscited by 2
  12. 2024HyperAggregation: Aggregating over Graph Edges with Hypernetworkscited by 1
  13. 2025A Decision-Based Heterogenous Graph Attention Network for Multi-Class Fake News Detectioncited by 1
  14. 2025gLSTM: Mitigating Over-Squashing by Increasing Storage Capacitycited by 1
  15. 2025Adaptive Graph Rewiring to Mitigate Over-Squashing in Mesh-Based GNNs for Fluid Dynamics Simulationscited by 1
  16. 2026MPM-LLM4DSE: Reaching the Pareto Frontier in HLS with Multimodal Learning and LLM-Driven Explorationcited by 1
  17. 2026Tensor-DTI: Enhancing Biomolecular Interaction Prediction with Contrastive Embedding Learningcited by 1
  18. 2024GEN: A Practical Alternative to Graph Transformers for Long-Range Graph Modelingcited by 0
  19. 2025TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flowscited by 0
  20. 2025Asynchronous Message Passing for Addressing Oversquashing in Graph Neural Networkscited by 0
  21. 2026Bayesian Neighborhood Adaptation for Graph Neural Networkscited by 0
  22. 2026Schreier-Coset Graph Rewiringcited by 0
  23. 2026Reference-Tail Trust:Certified Probability Floors for Learned Updates Inside a Deployed Networkcited by 0