mlsanityData to 7 Oct 2026

Graph Neural Networks Designed for Different Graph Types: A Survey

arXiv 2204.03080 ↗First posted 6 Apr 2022cs.LGcited by 21

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Abstract (arXiv)

Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. For this purpose, they can be defined as many different types which suitably reflect the individual contexts of the represented problem. To address cutting-edge problems based on graph data, the research field of Graph Neural Networks (GNNs) has emerged. Despite the field's youth and the speed at which new models are developed, many recent surveys have been published to keep track of them. Nevertheless, it has not yet been gathered which GNN can process what kind of graph types. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static and dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover, we distinguish between GNN models for discrete-time or continuous-time dynamic graphs and group the models according to their architecture. We find that there are still graph types that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 64 of its 146 references that are in the map. First posted on arXiv 6 Apr 2022.

Builds on

64 of 146 references.

References · 64

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Graph Attention Networkscited by 8,854
  4. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  5. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  6. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  7. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  8. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
  9. 2016Variational Graph Auto-Encoderscited by 1,373
  10. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
  11. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
  12. 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
38 more
  1. 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 878
  2. 2019Heterogeneous Graph Attention Networkcited by 732
  3. 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 521
  4. 2019Graph Transformer Networkscited by 520
  5. 2020Graph Neural Networks in Recommender Systems: A Surveycited by 493
  6. 2018Hypergraph Neural Networkscited by 482
  7. 2017Representation Learning on Graphs: Methods and Applicationscited by 456
  8. 2019EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphscited by 443
  9. 2019Predicting Dynamic Embedding Trajectory in Temporal Interaction Networkscited by 443
  10. 2018Deep Learning on Graphs: A Surveycited by 393
  11. 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 383
  12. 2016Structured Sequence Modeling with Graph Convolutional Recurrent Networkscited by 273
  13. 2017A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applicationscited by 260
  14. 2019Representation Learning for Dynamic Graphs: A Surveycited by 260
  15. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 260
  16. 2019Hyperbolic Graph Neural Networkscited by 244
  17. 2018Modeling polypharmacy side effects with graph convolutional networkscited by 237
  18. 2019Hypergraph Convolution and Hypergraph Attentioncited by 226
  19. 2017Graph Embedding Techniques, Applications, and Performance: A Surveycited by 188
  20. 2017Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphscited by 149
  21. 2017A Survey on Network Embeddingcited by 142
  22. 2021Hyperbolic Deep Neural Networks: A Surveycited by 138
  23. 2020Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Predictioncited by 132
  24. 2020Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A surveycited by 123
  25. 2020A Survey on The Expressive Power of Graph Neural Networkscited by 111
  26. 2021Fully Hyperbolic Neural Networkscited by 107
  27. 2021MagNet: A Neural Network for Directed Graphscited by 104
  28. 2018DynGEM: Deep Embedding Method for Dynamic Graphscited by 100
  29. 2021Learning Intents behind Interactions with Knowledge Graph for Recommendationcited by 94
  30. 2018Streaming Graph Neural Networkscited by 91
  31. 2017Dynamic Graph Convolutional Networkscited by 83
  32. 2019Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspectivecited by 73
  33. 2020Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networkscited by 73
  34. 2020JAKET: Joint Pre-training of Knowledge Graph and Language Understandingcited by 73
  35. 2021Directed Acyclic Graph Neural Networkscited by 70
  36. 2020Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networkscited by 64
  37. 2021Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identificationcited by 63
  38. 2021A Review of Graph Neural Networks and Their Applications in Power Systemscited by 62

50 most cited of 64 All 64 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 8; 2024: 6; 2025: 2; 2026: 2.

21 papers.

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  1. 2023Graph Foundation Models: Concepts, Opportunities and Challengescited by 101
  2. 2023Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunitiescited by 60
  3. 2023Graph Neural Networks for Text Classification: A Surveycited by 15
  4. 2024Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directionscited by 13
  5. 2022Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphscited by 12
  6. 2023Neural Atoms: Propagating Long-range Interaction in Molecular Graphs through Efficient Communication Channelcited by 10
  7. 2022A Hypergraph Neural Network Framework for Learning Hyperedge-Dependent Node Embeddingscited by 9
  8. 2023Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphscited by 9
  9. 2023Graph Neural Network for spatiotemporal data: methods and applicationscited by 6
  10. 2024Graph Reinforcement Learning for Power Grids: A Comprehensive Surveycited by 6
  11. 2024Survey of Graph Neural Network for Internet of Things and NextG Networkscited by 4
  12. 2024Utilizing Description Logics for Global Explanations of Heterogeneous Graph Neural Networkscited by 3
9 more
  1. 2023Tree Cross Attentioncited by 2
  2. 2022Marked Neural Spatio-Temporal Point Process Involving a Dynamic Graph Neural Networkcited by 1
  3. 2023DURENDAL: Graph deep learning framework for temporal heterogeneous networkscited by 1
  4. 2024Boolean-aware Boolean Circuit Classification: A Comprehensive Study on Graph Neural Networkcited by 1
  5. 2025Flow-Attentional Graph Neural Networkscited by 1
  6. 2026PiGGO: Physics-Guided Learnable Graph Kalman Filters for Virtual Sensing of Nonlinear Dynamic Structures under Uncertaintycited by 1
  7. 2024Analysing the Behaviour of Tree-Based Neural Networks in Regression Taskscited by 0
  8. 2025On the use of Graphs for Satellite Image Time Seriescited by 0
  9. 2026Coden: Efficient Temporal Graph Neural Networks for Continuous Predictioncited by 0