mlsanityData to 7 Oct 2026

Topology Adaptive Graph Convolutional Networks

arXiv 1710.10370 ↗First posted 28 Oct 2017cs.LG stat.MLcited by 112

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Abstract (arXiv)

Spectral graph convolutional neural networks (CNNs) require approximation to the convolution to alleviate the computational complexity, resulting in performance loss. This paper proposes the topology adaptive graph convolutional network (TAGCN), a novel graph convolutional network defined in the vertex domain. We provide a systematic way to design a set of fixed-size learnable filters to perform convolutions on graphs. The topologies of these filters are adaptive to the topology of the graph when they scan the graph to perform convolution. The TAGCN not only inherits the properties of convolutions in CNN for grid-structured data, but it is also consistent with convolution as defined in graph signal processing. Since no approximation to the convolution is needed, TAGCN exhibits better performance than existing spectral CNNs on a number of data sets and is also computationally simpler than other recent methods.

Key facts. Cited by 112 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Oct 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 17; 2022: 19; 2023: 23; 2024: 28; 2025: 14; 2026: 11.

112 papers.

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  1. 2021TDGIA:Effective Injection Attacks on Graph Neural Networkscited by 67
  2. 2021Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learningcited by 44
  3. 2021Chemical-Reaction-Aware Molecule Representation Learningcited by 36
  4. 2022On the Robustness of Graph Neural Diffusion to Topology Perturbationscited by 30
  5. 2021Distilling Holistic Knowledge with Graph Neural Networkscited by 26
  6. 2023Teacher-Student Architecture for Knowledge Distillation: A Surveycited by 26
  7. 2021Curvature Graph Neural Networkcited by 24
  8. 2022Bending Graphs: Hierarchical Shape Matching using Gated Optimal Transportcited by 24
  9. 2023Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approachcited by 24
  10. 2023ReLM: Leveraging Language Models for Enhanced Chemical Reaction Predictioncited by 24
  11. 2021Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learningcited by 21
  12. 2021Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendationcited by 21
38 more
  1. 2023Segmentation and Vascular Vectorization for Coronary Artery by Geometry-based Cascaded Neural Networkcited by 21
  2. 2023PowerFlowNet: Power Flow Approximation Using Message Passing Graph Neural Networkscited by 19
  3. 2023High Fidelity 3D Hand Shape Reconstruction via Scalable Graph Frequency Decompositioncited by 18
  4. 2022Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervisioncited by 16
  5. 2024Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FRONDcited by 16
  6. 2025Benchmarking Pretrained Molecular Embedding Models For Molecular Representation Learningcited by 15
  7. 2022Robust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label Propagationcited by 12
  8. 2023MG-GNN: Multigrid Graph Neural Networks for Learning Multilevel Domain Decomposition Methodscited by 12
  9. 2024MANTRA: The Manifold Triangulations Assemblagecited by 12
  10. 2022Learning Interface Conditions in Domain Decomposition Solverscited by 11
  11. 2023A Survey on Malware Detection with Graph Representation Learningcited by 11
  12. 2023S3M: Scalable Statistical Shape Modeling through Unsupervised Correspondencescited by 11
  13. 2024Temporal Graph ODEs for Irregularly-Sampled Time Seriescited by 11
  14. 2021Predicting Basin Stability of Power Grids using Graph Neural Networkscited by 10
  15. 2023PolyGNN: Polyhedron-based Graph Neural Network for 3D Building Reconstruction from Point Cloudscited by 10
  16. 2023A Metadata-Driven Approach to Understand Graph Neural Networkscited by 10
  17. 2024Coupling Graph Neural Networks with Fractional Order Continuous Dynamics: A Robustness Studycited by 9
  18. 2023A Graphical Approach to Document Layout Analysiscited by 8
  19. 2024When is an Embedding Model More Promising than Another?cited by 7
  20. 2021Shape-Biased Domain Generalization via Shock Graph Embeddingscited by 6
  21. 2021CGNN: Traffic Classification with Graph Neural Networkcited by 6
  22. 2021Spatial Graph Convolutional Neural Network via Structured Subdomain Adaptation and Domain Adversarial Learning for Bearing Fault Diagnosiscited by 6
  23. 2024Physics-Encoded Graph Neural Networks for Deformation Prediction under Contactcited by 6
  24. 2024A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizabilitycited by 6
  25. 2025Benchmarking Graph Neural Networks for Document Layout Analysis in Public Affairscited by 6
  26. 2022Towards better Interpretable and Generalizable AD detection using Collective Artificial Intelligencecited by 5
  27. 2023TAP: A Comprehensive Data Repository for Traffic Accident Prediction in Road Networkscited by 5
  28. 2023EX-Graph: A Pioneering Dataset Bridging Ethereum and Xcited by 5
  29. 2025Towards Generalization of Graph Neural Networks for AC Optimal Power Flowcited by 5
  30. 2021Room Classification on Floor Plan Graphs using Graph Neural Networkscited by 4
  31. 2023MolKD: Distilling Cross-Modal Knowledge in Chemical Reactions for Molecular Property Predictioncited by 4
  32. 2023Patch-wise Graph Contrastive Learning for Image Translationcited by 4
  33. 2025PFDelta: A Benchmark Dataset for Power Flow under Load, Generation, and Topology Variationscited by 4
  34. 2022Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural Networkscited by 3
  35. 2022Characterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying Glasscited by 3
  36. 2023Single Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learningcited by 3
  37. 2024Unleashing the Infinity Power of Geometry: A Novel Geometry-Aware Transformer (GOAT) for Whole Slide Histopathology Image Analysiscited by 3
  38. 2024Vision HgNN: An Electron-Micrograph is Worth Hypergraph of Hypernodescited by 3

50 most cited of 112 All 112 (JSON)