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Deep Convolutional Networks on Graph-Structured Data

arXiv 1506.05163 ↗First posted 16 Jun 2015cs.LG cs.CV cs.NEcited by 246

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Abstract (arXiv)

Deep Learning's recent successes have mostly relied on Convolutional Networks, which exploit fundamental statistical properties of images, sounds and video data: the local stationarity and multi-scale compositional structure, that allows expressing long range interactions in terms of shorter, localized interactions. However, there exist other important examples, such as text documents or bioinformatic data, that may lack some or all of these strong statistical regularities. In this paper we consider the general question of how to construct deep architectures with small learning complexity on general non-Euclidean domains, which are typically unknown and need to be estimated from the data. In particular, we develop an extension of Spectral Networks which incorporates a Graph Estimation procedure, that we test on large-scale classification problems, matching or improving over Dropout Networks with far less parameters to estimate.

Key facts. Cited by 246 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Jun 2015.

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Citing papers per year: 2021: 95; 2022: 58; 2023: 45; 2024: 22; 2025: 19; 2026: 7.

246 papers.

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  1. 2021GRAND: Graph Neural Diffusioncited by 258
  2. 2022Vision GNN: An Image is Worth Graph of Nodescited by 186
  3. 2022Graph neural networks for materials science and chemistrycited by 182
  4. 2021Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learningcited by 177
  5. 2021Training Graph Neural Networks with 1000 Layerscited by 169
  6. 2021Graph Learning: A Surveycited by 141
  7. 2021Hyperbolic Deep Neural Networks: A Surveycited by 139
  8. 2021Deep Generative Models in Engineering Design: A Reviewcited by 105
  9. 2022How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervisioncited by 102
  10. 2021TPCN: Temporal Point Cloud Networks for Motion Forecastingcited by 94
  11. 2024Graph Mamba: Towards Learning on Graphs with State Space Modelscited by 86
  12. 2021Beltrami Flow and Neural Diffusion on Graphscited by 84
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  1. 2021NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphscited by 73
  2. 2022Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labelscited by 63
  3. 2021Tensor Programs IIb: Architectural Universality of Neural Tangent Kernel Training Dynamicscited by 57
  4. 2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspectivecited by 56
  5. 2021Graph Few-shot Class-incremental Learningcited by 41
  6. 2021Sampling methods for efficient training of graph convolutional networks: A surveycited by 40
  7. 2021SportsCap: Monocular 3D Human Motion Capture and Fine-grained Understanding in Challenging Sports Videoscited by 40
  8. 2021Autobahn: Automorphism-based Graph Neural Netscited by 39
  9. 2021p-Laplacian Based Graph Neural Networkscited by 39
  10. 2022FMP: Toward Fair Graph Message Passing against Topology Biascited by 35
  11. 2023SAM-Assisted Remote Sensing Imagery Semantic Segmentation with Object and Boundary Constraintscited by 35
  12. 2021FedGL: Federated Graph Learning Framework with Global Self-Supervisioncited by 34
  13. 2021RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Imagescited by 34
  14. 2023PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Drivingcited by 33
  15. 2021Network of Tensor Time Seriescited by 32
  16. 2022Graph Lifelong Learning: A Surveycited by 30
  17. 2023DAG Matters! GFlowNets Enhanced Explainer For Graph Neural Networkscited by 27
  18. 2021Learning Conjoint Attentions for Graph Neural Netscited by 26
  19. 2022DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognitioncited by 26
  20. 2023PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detectioncited by 25
  21. 2021Curvature Graph Neural Networkcited by 24
  22. 2022Graph Attention Retrospectivecited by 24
  23. 2022Bootstrap Motion Forecasting With Self-Consistent Constraintscited by 23
  24. 2021Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networkscited by 22
  25. 2023Data-centric Graph Learning: A Surveycited by 22
  26. 2021Hybrid Quantum-Classical Graph Convolutional Networkcited by 21
  27. 2021Graph coarsening: From scientific computing to machine learningcited by 21
  28. 2022Graph Neural Networks for Moleculescited by 21
  29. 2021Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognitioncited by 20
  30. 2021Multi-modal Entity Alignment in Hyperbolic Spacecited by 19
  31. 2021Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networkscited by 19
  32. 2021Orthogonal Graph Neural Networkscited by 19
  33. 2021SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Predictioncited by 19
  34. 2022Feature Overcorrelation in Deep Graph Neural Networks: A New Perspectivecited by 19
  35. 2021Edge-Featured Graph Attention Networkcited by 18
  36. 2023DEGREE: Decomposition Based Explanation For Graph Neural Networkscited by 18
  37. 2022Learning Resilient Radio Resource Management Policies with Graph Neural Networkscited by 17
  38. 2022Graph Neural Network and Spatiotemporal Transformer Attention for 3D Video Object Detection from Point Cloudscited by 17

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