mlsanityData to 7 Oct 2026

A Survey on Graph Representation Learning Methods

arXiv 2204.01855 ↗First posted 4 Apr 2022cs.LG cs.SIcited by 66

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Abstract (arXiv)

Graphs representation learning has been a very active research area in recent years. The goal of graph representation learning is to generate graph representation vectors that capture the structure and features of large graphs accurately. This is especially important because the quality of the graph representation vectors will affect the performance of these vectors in downstream tasks such as node classification, link prediction and anomaly detection. Many techniques are proposed for generating effective graph representation vectors. Two of the most prevalent categories of graph representation learning are graph embedding methods without using graph neural nets (GNN), which we denote as non-GNN based graph embedding methods, and graph neural nets (GNN) based methods. Non-GNN graph embedding methods are based on techniques such as random walks, temporal point processes and neural network learning methods. GNN-based methods, on the other hand, are the application of deep learning on graph data. In this survey, we provide an overview of these two categories and cover the current state-of-the-art methods for both static and dynamic graphs. Finally, we explore some open and ongoing research directions for future work.

Key facts. Cited by 66 papers in the map (data through 7 Oct 2026). Builds on 211 of its 363 references that are in the map. First posted on arXiv 4 Apr 2022.

Builds on

211 of 363 references.

References · 211

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  1. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
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50 most cited of 211 All 211 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 5; 2024: 16; 2025: 26; 2026: 14.

66 papers.

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  2. 2023Graph Anomaly Detection in Time Series: A Surveycited by 18
  3. 2023HGWaveNet: A Hyperbolic Graph Neural Network for Temporal Link Predictioncited by 13
  4. 2024Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learningcited by 12
  5. 2022Learnable Spectral Wavelets on Dynamic Graphs to Capture Global Interactionscited by 10
  6. 2024Two Heads Are Better Than One: Integrating Knowledge from Knowledge Graphs and Large Language Models for Entity Alignmentcited by 8
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  9. 2025You Are What You Bought: Generating Customer Personas for E-commerce Applicationscited by 7
  10. 2025A Survey of Link Prediction in Temporal Networkscited by 6
  11. 2024Explainable Graph Neural Networks Under Firecited by 5
  12. 2025Beyond Fixed Depth: Adaptive Graph Neural Networks for Node Classification Under Varying Homophilycited by 5
38 more
  1. 2022A Survey on Temporal Graph Representation Learning and Generative Modelingcited by 4
  2. 2022Machine Learning Partners in Criminal Networkscited by 4
  3. 2024A Unified Framework for Combinatorial Optimization Based on Graph Neural Networkscited by 4
  4. 2024DeepSN: A Sheaf Neural Framework for Influence Maximizationcited by 4
  5. 2025GraphiT: Efficient Node Classification on Text-Attributed Graphs with Prompt Optimized LLMscited by 4
  6. 2024Unveiling Privacy Vulnerabilities: Investigating the Role of Structure in Graph Datacited by 3
  7. 2024What comes after transformers? -- A selective survey connecting ideas in deep learningcited by 3
  8. 2025Multi-Objective Bayesian Optimization for Networked Black-Box Systems: A Path to Greener Profits and Smarter Designscited by 3
  9. 2025Incorporating Spatial Information into Goal-Conditioned Hierarchical Reinforcement Learning via Graph Representationscited by 3
  10. 2023Feature Expansion for Graph Neural Networkscited by 2
  11. 2023LIC-GAN: Language Information Conditioned Graph Generative GAN Modelcited by 2
  12. 2024Efficient and Effective Implicit Dynamic Graph Neural Networkcited by 2
  13. 2025Hybrid-Collaborative Augmentation and Contrastive Sample Adaptive-Differential Awareness for Robust Attributed Graph Clusteringcited by 2
  14. 2025Representation of the structure of graphs by sequences of instructionscited by 2
  15. 2026Hyperbolic Graph Neural Networks Under the Microscope: The Role of Geometry-Task Alignmentcited by 2
  16. 2026DSBD: Dual-Aligned Structural Basis Distillation for Graph Domain Adaptationcited by 2
  17. 2026Node4All: Learning Node Representation Beyond Datasetscited by 2
  18. 2023Topology Only Pre-Training: Towards Generalised Multi-Domain Graph Modelscited by 1
  19. 2024Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignmentscited by 1
  20. 2024Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine Learningcited by 1
  21. 2024Local Intrinsic Dimensionality for Dynamic Graph Embeddingscited by 1
  22. 2025Structure-preserving contrastive learning for spatial time seriescited by 1
  23. 2025Grasp the Graph (GtG) 2.0: Ensemble of Graph Neural Networks for High-Precision Grasp Pose Detection in Cluttercited by 1
  24. 2025T-TExTS (Teaching Text Expansion for Teacher Scaffolding): Enhancing Text Selection in High School Literature through Knowledge Graph-Based Recommendationcited by 1
  25. 2025Graph-based Fake Account Detection: A Surveycited by 1
  26. 2025Towards Unsupervised Open-Set Graph Domain Adaptation via Dual Reprogrammingcited by 1
  27. 2026Instruction set for the representation of graphscited by 1
  28. 2026GraPHFormer: A Multimodal Graph Persistent Homology Transformer for the Analysis of Neuroscience Morphologiescited by 1
  29. 2022Graph sampling for node embeddingcited by 0
  30. 2024Unsupervised Optimisation of GNNs for Node Clusteringcited by 0
  31. 2024MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs Embeddingcited by 0
  32. 2024Solving the Tree Containment Problem Using Graph Neural Networkscited by 0
  33. 2024Enhancing Graph Self-Supervised Learning with Graph Interplaycited by 0
  34. 2025Human-Centric Community Detection in Hybrid Metaverse Networks with Integrated AI Entitiescited by 0
  35. 2025Co-Evolution-Based Metal-Binding Residue Prediction with Graph Neural Networkscited by 0
  36. 2025ffstruc2vec: Flat, Flexible and Scalable Learning of Node Representations from Structural Identitiescited by 0
  37. 2025DG-STMTL: A Novel Graph Convolutional Network for Multi-Task Spatio-Temporal Traffic Forecastingcited by 0
  38. 2025Evidential Spectrum-Aware Contrastive Learning for OOD Detection in Dynamic Graphscited by 0

50 most cited of 66 All 66 (JSON)