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Adaptive Graph Convolutional Neural Networks

arXiv 1801.03226 ↗First posted 10 Jan 2018cs.LG stat.MLcited by 155

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Abstract (arXiv)

Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure. However, for most real data, the graph structures varies in both size and connectivity. The paper proposes a generalized and flexible graph CNN taking data of arbitrary graph structure as input. In that way a task-driven adaptive graph is learned for each graph data while training. To efficiently learn the graph, a distance metric learning is proposed. Extensive experiments on nine graph-structured datasets have demonstrated the superior performance improvement on both convergence speed and predictive accuracy.

Key facts. Cited by 155 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Jan 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 40; 2022: 36; 2023: 32; 2024: 23; 2025: 17; 2026: 7.

155 papers.

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  1. 2021Training Graph Neural Networks with 1000 Layerscited by 169
  2. 2021SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networkscited by 110
  3. 2021Constructing Stronger and Faster Baselines for Skeleton-based Action Recognitioncited by 87
  4. 2021Mining Latent Structures for Multimedia Recommendationcited by 79
  5. 2021SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanismcited by 75
  6. 2022P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Promptingcited by 74
  7. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  8. 2021A Survey on Graph Structure Learning: Progress and Opportunitiescited by 71
  9. 2022A Survey on Graph Representation Learning Methodscited by 66
  10. 2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspectivecited by 56
  11. 2021Conditional Directed Graph Convolution for 3D Human Pose Estimationcited by 52
  12. 2023GLA-GCN: Global-local Adaptive Graph Convolutional Network for 3D Human Pose Estimation from Monocular Videocited by 47
38 more
  1. 2021AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filtercited by 43
  2. 2021SportsCap: Monocular 3D Human Motion Capture and Fine-grained Understanding in Challenging Sports Videoscited by 40
  3. 2021Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networkscited by 38
  4. 2023SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimizationcited by 35
  5. 2021Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detectioncited by 34
  6. 2022Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNNcited by 34
  7. 2024KTPFormer: Kinematics and Trajectory Prior Knowledge-Enhanced Transformer for 3D Human Pose Estimationcited by 34
  8. 2022Evidence-aware Fake News Detection with Graph Neural Networkscited by 30
  9. 2022Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Modelscited by 29
  10. 2021SPAGAN: Shortest Path Graph Attention Networkcited by 28
  11. 2024Segment Anything Model for Road Network Graph Extractioncited by 28
  12. 2021DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinationscited by 27
  13. 2023ChatGraph: Interpretable Text Classification by Converting ChatGPT Knowledge to Graphscited by 27
  14. 2021Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networkscited by 25
  15. 2022Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approachcited by 23
  16. 2023Data-centric Graph Learning: A Surveycited by 22
  17. 2022Graph Neural Networks for Moleculescited by 21
  18. 2021Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognitioncited by 20
  19. 2021Orthogonal Graph Neural Networkscited by 19
  20. 2021Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendationcited by 18
  21. 2022Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networkscited by 18
  22. 2022TIDE: Time Derivative Diffusion for Deep Learning on Graphscited by 17
  23. 2021Learnable Hypergraph Laplacian for Hypergraph Learningcited by 16
  24. 2023Segment Anything in Non-Euclidean Domains: Challenges and Opportunitiescited by 16
  25. 2021Beyond Low-pass Filtering: Graph Convolutional Networks with Automatic Filteringcited by 14
  26. 2021Learning Feature Aggregation for Deep 3D Morphable Modelscited by 13
  27. 2021GraphFM: Graph Factorization Machines for Feature Interaction Modelingcited by 13
  28. 2021Attribute Artifacts Removal for Geometry-based Point Cloud Compressioncited by 13
  29. 2024Simba: Mamba augmented U-ShiftGCN for Skeletal Action Recognition in Videoscited by 13
  30. 2024EvGNN: An Event-driven Graph Neural Network Accelerator for Edge Visioncited by 13
  31. 2023Urban Regional Function Guided Traffic Flow Predictioncited by 12
  32. 2023Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detectioncited by 12
  33. 2023GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networkscited by 12
  34. 2021Learning Graph Neural Networks with Positive and Unlabeled Nodescited by 11
  35. 2022SemAffiNet: Semantic-Affine Transformation for Point Cloud Segmentationcited by 11
  36. 2021Adversarial Attack Framework on Graph Embedding Models with Limited Knowledgecited by 10
  37. 2022Signed Graph Neural Networks: A Frequency Perspectivecited by 10
  38. 2022Graph Learning with Localized Neighborhood Fairnesscited by 10

50 most cited of 155 All 155 (JSON)