mlsanityData to 7 Oct 2026

A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications

arXiv 1709.07604 ↗First posted 22 Sep 2017cs.AIcited by 260

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Abstract (arXiv)

Graph is an important data representation which appears in a wide diversity of real-world scenarios. Effective graph analytics provides users a deeper understanding of what is behind the data, and thus can benefit a lot of useful applications such as node classification, node recommendation, link prediction, etc. However, most graph analytics methods suffer the high computation and space cost. Graph embedding is an effective yet efficient way to solve the graph analytics problem. It converts the graph data into a low dimensional space in which the graph structural information and graph properties are maximally preserved. In this survey, we conduct a comprehensive review of the literature in graph embedding. We first introduce the formal definition of graph embedding as well as the related concepts. After that, we propose two taxonomies of graph embedding which correspond to what challenges exist in different graph embedding problem settings and how the existing work address these challenges in their solutions. Finally, we summarize the applications that graph embedding enables and suggest four promising future research directions in terms of computation efficiency, problem settings, techniques and application scenarios.

Key facts. Cited by 260 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Sep 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 78; 2022: 49; 2023: 49; 2024: 48; 2025: 24; 2026: 12.

260 papers.

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  1. 2023GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networkscited by 166
  2. 2021Graph Learning: A Surveycited by 140
  3. 2022Deep Clustering: A Comprehensive Surveycited by 78
  4. 2021SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanismcited by 75
  5. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  6. 2022A Survey on Graph Representation Learning Methodscited by 66
  7. 2021A Survey on Embedding Dynamic Graphscited by 60
  8. 2021E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTcited by 55
  9. 2022Instance Relation Graph Guided Source-Free Domain Adaptive Object Detectioncited by 55
  10. 2021ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph Networkscited by 51
  11. 2022Hyperbolic Graph Neural Networks: A Review of Methods and Applicationscited by 40
  12. 2023MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphscited by 40
38 more
  1. 2023Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on Graphscited by 39
  2. 2021Automated Machine Learning on Graphs: A Surveycited by 38
  3. 2021Rethinking Graph Auto-Encoder Models for Attributed Graph Clusteringcited by 37
  4. 2023HINormer: Representation Learning On Heterogeneous Information Networks with Graph Transformercited by 35
  5. 2021FedGL: Federated Graph Learning Framework with Global Self-Supervisioncited by 34
  6. 2021Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detectioncited by 34
  7. 2021Block Modeling-Guided Graph Convolutional Neural Networkscited by 32
  8. 2024Graph Transformers: A Surveycited by 32
  9. 2022Graph Lifelong Learning: A Surveycited by 30
  10. 2021Marius: Learning Massive Graph Embeddings on a Single Machinecited by 29
  11. 2022Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysiscited by 29
  12. 2024A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challengescited by 29
  13. 2023On Generalized Degree Fairness in Graph Neural Networkscited by 28
  14. 2022Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networkscited by 25
  15. 2022Contrastive Laplacian Eigenmapscited by 24
  16. 2023Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Designcited by 22
  17. 2021Graph-based Deep Learning for Communication Networks: A Surveycited by 21
  18. 2022Graph Neural Networks Designed for Different Graph Types: A Surveycited by 21
  19. 2023HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understandingcited by 21
  20. 2024Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Miningcited by 20
  21. 2021Network Representation Learning: From Preprocessing, Feature Extraction to Node Embeddingcited by 19
  22. 2023High-Throughput Vector Similarity Search in Knowledge Graphscited by 19
  23. 2022Saga: A Platform for Continuous Construction and Serving of Knowledge At Scalecited by 18
  24. 2021Neural ranking models for document retrievalcited by 16
  25. 2021Meta-Inductive Node Classification across Graphscited by 16
  26. 2021HW2VEC: A Graph Learning Tool for Automating Hardware Securitycited by 16
  27. 2021Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methodscited by 16
  28. 2024Advancing Graph Representation Learning with Large Language Models: A Comprehensive Survey of Techniquescited by 16
  29. 2021Foundations of Population-Based SHM, Part IV: The Geometry of Spaces of Structures and their Feature Spacescited by 15
  30. 2021Link Prediction on N-ary Relational Data Based on Relatedness Evaluationcited by 15
  31. 2021Convolutional Hypercomplex Embeddings for Link Predictioncited by 15
  32. 2021A Broader Picture of Random-walk Based Graph Embeddingcited by 15
  33. 2023Dynamic Graph Representation Learning with Neural Networks: A Surveycited by 15
  34. 2023A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directionscited by 15
  35. 2022CenGCN: Centralized Convolutional Networks with Vertex Imbalance for Scale-Free Graphscited by 14
  36. 2024InstructG2I: Synthesizing Images from Multimodal Attributed Graphscited by 14
  37. 2021Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networkscited by 13
  38. 2021Poisoning Knowledge Graph Embeddings via Relation Inference Patternscited by 12

50 most cited of 260 All 260 (JSON)