mlsanityData to 14 Oct 2026

Graph Representation Learning: A Survey

arXiv 1909.00958 ↗First posted 3 Sep 2019cs.LG cs.SI stat.MLcited by 75

Not on the map. Why?

Abstract (arXiv)

Research on graph representation learning has received a lot of attention in recent years since many data in real-world applications come in form of graphs. High-dimensional graph data are often in irregular form, which makes them more difficult to analyze than image/video/audio data defined on regular lattices. Various graph embedding techniques have been developed to convert the raw graph data into a low-dimensional vector representation while preserving the intrinsic graph properties. In this review, we first explain the graph embedding task and its challenges. Next, we review a wide range of graph embedding techniques with insights. Then, we evaluate several state-of-the-art methods against small and large datasets and compare their performance. Finally, potential applications and future directions are presented.

Key facts. Cited by 75 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Sep 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

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

75 papers.

Sort:

By area:

  1. 2022A Survey of Machine Unlearningcited by 312
  2. 2021Graph Representation Learning in Biomedicinecited by 80
  3. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  4. 2022A Survey on Graph Representation Learning Methodscited by 66
  5. 2022Transformers in Medical Image Analysis: A Reviewcited by 41
  6. 2024Graph Transformers: A Surveycited by 32
  7. 2022MGAE: Masked Autoencoders for Self-Supervised Learning on Graphscited by 28
  8. 2024A survey of dynamic graph neural networkscited by 24
  9. 2022Deep Generative Model for Periodic Graphscited by 20
  10. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20
  11. 2023Graph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution Generalizationcited by 18
  12. 2022How Expressive are Transformers in Spectral Domain for Graphs?cited by 15
38 more
  1. 2023Search to Fine-tune Pre-trained Graph Neural Networks for Graph-level Taskscited by 13
  2. 2023A Survey on Malware Detection with Graph Representation Learningcited by 11
  3. 2021Recommender systems based on graph embedding techniques: A comprehensive reviewcited by 8
  4. 2022Towards Generalizable Graph Contrastive Learning: An Information Theory Perspectivecited by 8
  5. 2022Swinv2-Imagen: Hierarchical Vision Transformer Diffusion Models for Text-to-Image Generationcited by 7
  6. 2023Hyperbolic Graph Diffusion Modelcited by 7
  7. 2024DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalizationcited by 7
  8. 2024A Community Detection and Graph Neural Network Based Link Prediction Approach for Scientific Literaturecited by 6
  9. 2021Persistent Homology and Graphs Representation Learningcited by 5
  10. 2022Empirical Evaluation and Theoretical Analysis for Representation Learning: A Surveycited by 5
  11. 2022Robust Graph Representation Learning via Predictive Codingcited by 5
  12. 2024Improving out-of-distribution generalization in graphs via hierarchical semantic environmentscited by 5
  13. 2024Explainable Graph Neural Networks Under Firecited by 5
  14. 2021Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention Networkscited by 4
  15. 2022mathsfG2Retro as a Two-Step Graph Generative Models for Retrosynthesis Predictioncited by 4
  16. 2022A Survey on Temporal Graph Representation Learning and Generative Modelingcited by 4
  17. 2023A Comprehensive Survey on Graph Summarization with Graph Neural Networkscited by 4
  18. 2023Independent Distribution Regularization for Private Graph Embeddingcited by 4
  19. 2024Graph Neural Backdoor: Fundamentals, Methodologies, Applications, and Future Directionscited by 4
  20. 2025HGCN(O): A Self-Tuning GCN HyperModel Toolkit for Outcome Prediction in Event-Sequence Datacited by 4
  21. 2022Combining Intra-Risk and Contagion Risk for Enterprise Bankruptcy Prediction Using Graph Neural Networkscited by 3
  22. 2023H2TNE: Temporal Heterogeneous Information Network Embedding in Hyperbolic Spacescited by 3
  23. 2023Connector 0.5: A unified framework for graph representation learningcited by 3
  24. 2023Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approachcited by 3
  25. 2023Contrastive Learning for Non-Local Graphs with Multi-Resolution Structural Viewscited by 3
  26. 2024VeTraSS: Vehicle Trajectory Similarity Search Through Graph Modeling and Representation Learningcited by 3
  27. 2025Incorporating Spatial Information into Goal-Conditioned Hierarchical Reinforcement Learning via Graph Representationscited by 3
  28. 2026LEDA: Latent Semantic Distribution Alignment for Multi-domain Graph Pre-trainingcited by 3
  29. 2021Exact Recovery of Community Structures Using DeepWalk and Node2veccited by 2
  30. 2021Label Propagation across Graphs: Node Classification using Graph Neural Tangent Kernelscited by 2
  31. 2023Zoo Guide to Network Embeddingcited by 2
  32. 2023An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problemscited by 2
  33. 2024Link-aware link prediction over temporal graph by pattern recognitioncited by 2
  34. 2024Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysiscited by 2
  35. 2024Network Topology Inference from Smooth Signals Under Partial Observabilitycited by 2
  36. 2025Hybrid-Hierarchical Fashion Graph Attention Network for Compatibility-Oriented and Personalized Outfit Recommendationcited by 2
  37. 2026Node4All: Learning Node Representation Beyond Datasetscited by 2
  38. 2021GGT: Graph-Guided Testing for Adversarial Sample Detection of Deep Neural Networkcited by 1

50 most cited of 75 All 75 (JSON)