mlsanityData to 7 Oct 2026

Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction

arXiv 2206.04216 ↗First posted 9 Jun 2022cs.LG cs.AIcited by 78

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Abstract (arXiv)

Graph Neural Networks (GNNs) have been widely applied to various fields for learning over graph-structured data. They have shown significant improvements over traditional heuristic methods in various tasks such as node classification and graph classification. However, since GNNs heavily rely on smoothed node features rather than graph structure, they often show poor performance than simple heuristic methods in link prediction where the structural information, e.g., overlapped neighborhoods, degrees, and shortest paths, is crucial. To address this limitation, we propose Neighborhood Overlap-aware Graph Neural Networks (Neo-GNNs) that learn useful structural features from an adjacency matrix and estimate overlapped neighborhoods for link prediction. Our Neo-GNNs generalize neighborhood overlap-based heuristic methods and handle overlapped multi-hop neighborhoods. Our extensive experiments on Open Graph Benchmark datasets (OGB) demonstrate that Neo-GNNs consistently achieve state-of-the-art performance in link prediction. Our code is publicly available at https://github.com/seongjunyun/Neo_GNNs.

Key facts. Cited by 78 papers in the map (data through 7 Oct 2026). Builds on 31 of its 51 references that are in the map. First posted on arXiv 9 Jun 2022.

Builds on

31 of 51 references.

References · 31

  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. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  5. 2018How Powerful are Graph Neural Networks?cited by 4,355
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  11. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  12. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
19 more
  1. 2016Variational Graph Auto-Encoderscited by 1,373
  2. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 875
  3. 2020Simple and Deep Graph Convolutional Networkscited by 857
  4. 2018Link Prediction Based on Graph Neural Networkscited by 829
  5. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 786
  6. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
  7. 2018Junction Tree Variational Autoencoder for Molecular Graph Generationcited by 647
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  9. 2019Diffusion Improves Graph Learningcited by 414
  10. 2018MolGAN: An implicit generative model for small molecular graphscited by 392
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 19; 2024: 25; 2025: 19; 2026: 10.

78 papers.

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  1. 2024Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classificationcited by 83
  2. 2022Graph Neural Networks for Link Prediction with Subgraph Sketchingcited by 81
  3. 2023Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarkingcited by 69
  4. 2023Neural Common Neighbor with Completion for Link Predictioncited by 64
  5. 2022Linkless Link Prediction via Relational Distillationcited by 39
  6. 2023Revisiting Link Prediction: A Data Perspectivecited by 29
  7. 2023Pure Message Passing Can Estimate Common Neighbor for Link Predictioncited by 18
  8. 2024Efficient Neural Common Neighbor for Temporal Graph Link Predictioncited by 18
  9. 2023LPFormer: An Adaptive Graph Transformer for Link Predictioncited by 17
  10. 2024Graph Sparsification via Mixture of Graphscited by 17
  11. 2023GrOVe: Ownership Verification of Graph Neural Networks using Embeddingscited by 16
  12. 2023Motif-aware Attribute Masking for Molecular Graph Pre-trainingcited by 16
38 more
  1. 2024PageRank Bandits for Link Predictioncited by 14
  2. 2023A Topological Perspective on Demystifying GNN-Based Link Prediction Performancecited by 11
  3. 2024Mixture of Link Predictors on Graphscited by 10
  4. 2024Node Identifiers: Compact, Discrete Representations for Efficient Graph Learningcited by 10
  5. 2024On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networkscited by 10
  6. 2024TAGLAS: An atlas of text-attributed graph datasets in the era of large graph and language modelscited by 9
  7. 2024Optimizing Long-tailed Link Prediction in Graph Neural Networks through Structure Representation Enhancementcited by 9
  8. 2025TxPert: Leveraging Biochemical Relationships for Out-of-Distribution Transcriptomic Perturbation Predictioncited by 9
  9. 2022Deformable Graph Transformercited by 7
  10. 2023NuTrea: Neural Tree Search for Context-guided Multi-hop KGQAcited by 7
  11. 2024Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmarkcited by 7
  12. 2023Efficient Link Prediction via GNN Layers Induced by Negative Samplingcited by 6
  13. 2023Causal Lifting and Link Predictioncited by 5
  14. 2024Accurate Link Prediction for Edge-Incomplete Graphs via PU Learningcited by 5
  15. 2026TFMLinker: Universal Link Predictor by Graph In-Context Learning with Tabular Foundation Modelscited by 5
  16. 2023You Only Transfer What You Share: Intersection-Induced Graph Transfer Learning for Link Predictioncited by 4
  17. 2023Learning Scalable Structural Representations for Link Prediction with Bloom Signaturescited by 4
  18. 2024TEDDY: Trimming Edges with Degree-based Discrimination strategYcited by 4
  19. 2024Node Duplication Improves Cold-start Link Predictioncited by 4
  20. 2024Reconsidering the Performance of GAE in Link Predictioncited by 4
  21. 2025Bridging Theory and Practice in Link Representation with Graph Neural Networkscited by 4
  22. 2023Link Prediction without Graph Neural Networkscited by 3
  23. 2024GENIE: Watermarking Graph Neural Networks for Link Predictioncited by 3
  24. 2024Link Prediction with Untrained Message Passing Layerscited by 3
  25. 2025PersonalizedRouter: Personalized LLM Routing via Graph-based User Preference Modelingcited by 3
  26. 2024PHLP: Sole Persistent Homology for Link Prediction - Interpretable Feature Extractioncited by 2
  27. 2025Toward Scalable Graph Unlearning: A Node Influence Maximization based Approachcited by 2
  28. 2025Using Random Noise Equivariantly to Boost Graph Neural Networks Universallycited by 2
  29. 2025Open Your Eyes: Vision Enhances Message Passing Neural Networks in Link Predictioncited by 2
  30. 2025Understanding Generalization in Node and Link Predictioncited by 2
  31. 2025A Scalable Pretraining Framework for Link Prediction with Efficient Adaptationcited by 2
  32. 2022SPGP: Structure Prototype Guided Graph Poolingcited by 1
  33. 2022A Node-collaboration-informed Graph Convolutional Network for Precise Representation to Undirected Weighted Graphscited by 1
  34. 2023A Network Science perspective of Graph Convolutional Networks: A surveycited by 1
  35. 2023Curriculum-Enhanced Residual Soft An-Isotropic Normalization for Over-smoothness in Deep GNNscited by 1
  36. 2024Derivation of Back-propagation for Graph Convolutional Networks using Matrix Calculus and its Application to Explainable Artificial Intelligencecited by 1
  37. 2024Neighbor Overlay-Induced Graph Attention Networkcited by 1
  38. 2024PROXI: Challenging the GNNs for Link Predictioncited by 1

50 most cited of 78 All 78 (JSON)