mlsanityData to 7 Oct 2026

Graph Neural Networks: Taxonomy, Advances and Trends

arXiv 2012.08752 ↗First posted 16 Dec 2020cs.LGcited by 48

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Abstract (arXiv)

Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-dimensional spaces according to specific tasks. Up to now, there have been several surveys on this topic. However, they usually lay emphasis on different angles so that the readers can not see a panorama of the graph neural networks. This survey aims to overcome this limitation, and provide a comprehensive review on the graph neural networks. First of all, we provide a novel taxonomy for the graph neural networks, and then refer to up to 400 relevant literatures to show the panorama of the graph neural networks. All of them are classified into the corresponding categories. In order to drive the graph neural networks into a new stage, we summarize four future research directions so as to overcome the facing challenges. It is expected that more and more scholars can understand and exploit the graph neural networks, and use them in their research community.

Key facts. Cited by 48 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Dec 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 5; 2023: 8; 2024: 16; 2025: 11; 2026: 5.

48 papers.

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  1. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  2. 2024Towards Effective and General Graph Unlearning via Mutual Evolutioncited by 29
  3. 2022Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applicationscited by 27
  4. 2024AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneitycited by 25
  5. 2024Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspectivecited by 22
  6. 2023Permutation Equivariant Graph Framelets for Heterophilous Graph Learningcited by 19
  7. 2023Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classificationcited by 19
  8. 2023Graph Anomaly Detection in Time Series: A Surveycited by 18
  9. 2024A review of graph neural network applications in mechanics-related domainscited by 14
  10. 2024Tensor-view Topological Graph Neural Networkcited by 12
  11. 2024Two Heads Are Better Than One: Integrating Knowledge from Knowledge Graphs and Large Language Models for Entity Alignmentcited by 8
  12. 2025OpenGU: A Comprehensive Benchmark for Graph Unlearningcited by 8
36 more
  1. 2023Strengthening structural baselines for graph classification using Local Topological Profilecited by 7
  2. 2021Structure-Aware Hierarchical Graph Pooling using Information Bottleneckcited by 6
  3. 2022Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learningcited by 6
  4. 2023Faithful and Consistent Graph Neural Network Explanations with Rationale Alignmentcited by 6
  5. 2023Entropy Causal Graphs for Multivariate Time Series Anomaly Detectioncited by 6
  6. 2025ClusterViG: Efficient Globally Aware Vision GNNs via Image Partitioningcited by 6
  7. 2021Node Feature Kernels Increase Graph Convolutional Network Robustnesscited by 5
  8. 2024Disambiguated Node Classification with Graph Neural Networkscited by 4
  9. 2024Survey of Graph Neural Network for Internet of Things and NextG Networkscited by 4
  10. 2025Aggregate to Adapt: Node-Centric Aggregation for Multi-Source-Free Graph Domain Adaptationcited by 4
  11. 2022Twin Weisfeiler-Lehman: High Expressive GNNs for Graph Classificationcited by 3
  12. 2024Beyond Grid Data: Exploring Graph Neural Networks for Earth Observationcited by 3
  13. 2025Hypergraph Neural Network with State Space Models for Node Classificationcited by 3
  14. 2024When Witnesses Defend: A Witness Graph Topological Layer for Adversarial Graph Learningcited by 2
  15. 2024Towards Data-centric Machine Learning on Directed Graphs: a Surveycited by 2
  16. 2025Partition-wise Graph Filtering: A Unified Perspective Through the Lens of Graph Coarseningcited by 2
  17. 2026CAMPA: Efficient and Aligned Multimodal Graph Learning via Decoupled Propagation and Aggregationcited by 2
  18. 2021CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data Augmentationscited by 1
  19. 2022Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspectivecited by 1
  20. 2022Graph Fuzzy System: Concepts, Models and Algorithmscited by 1
  21. 2023Optimizing Ego Vehicle Trajectory Prediction: The Graph Enhancement Approachcited by 1
  22. 2024Polynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices Approachcited by 1
  23. 2025Learnable quantum spectral filters for hybrid graph neural networkscited by 1
  24. 2026Attention-based graph neural networks: a surveycited by 1
  25. 2024Inferring Properties of Graph Neural Networkscited by 0
  26. 2024Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networkscited by 0
  27. 2024BetaExplainer: A Probabilistic Method to Explain Graph Neural Networkscited by 0
  28. 2024Introduction to Graph Neural Networks for Machine Learning Engineerscited by 0
  29. 2025Topology-Driven Attribute Recovery for Attribute Missing Graph Learning in Social Internet of Thingscited by 0
  30. 2025Atherosclerosis through Hierarchical Explainable Neural Network Analysiscited by 0
  31. 2025Temporal social network modeling of mobile connectivity data with graph neural networkscited by 0
  32. 2025Rethinking Graph Domain Adaptation: A Spectral Contrastive Perspectivecited by 0
  33. 2025Optimizing Predictive Maintenance in Intelligent Manufacturing: An Integrated FNO-DAE-GNN-PPO MDP Frameworkcited by 0
  34. 2026Both Topology and Text Matter: Revisiting LLM-guided Out-of-Distribution Detection on Text-attributed Graphscited by 0
  35. 2026Towards Modality-imbalanced Federated Graph Learning: A Data Synthesis-based Approachcited by 0
  36. 2026Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancementcited by 0