mlsanityData to 14 Oct 2026

struc2vec: Learning Node Representations from Structural Identity

arXiv 1704.03165 ↗First posted 11 Apr 2017cs.SI cs.LG stat.MLcited by 215

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Abstract (arXiv)

Structural identity is a concept of symmetry in which network nodes are identified according to the network structure and their relationship to other nodes. Structural identity has been studied in theory and practice over the past decades, but only recently has it been addressed with representational learning techniques. This work presents struc2vec, a novel and flexible framework for learning latent representations for the structural identity of nodes. struc2vec uses a hierarchy to measure node similarity at different scales, and constructs a multilayer graph to encode structural similarities and generate structural context for nodes. Numerical experiments indicate that state-of-the-art techniques for learning node representations fail in capturing stronger notions of structural identity, while struc2vec exhibits much superior performance in this task, as it overcomes limitations of prior approaches. As a consequence, numerical experiments indicate that struc2vec improves performance on classification tasks that depend more on structural identity.

Key facts. Cited by 215 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Apr 2017.

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Citing papers per year: 2021: 53; 2022: 38; 2023: 32; 2024: 30; 2025: 40; 2026: 22.

215 papers.

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  1. 2021Adversarial Graph Augmentation to Improve Graph Contrastive Learningcited by 200
  2. 2021Graph Learning: A Surveycited by 141
  3. 2021Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patternscited by 95
  4. 2021EDITS: Modeling and Mitigating Data Bias for Graph Neural Networkscited by 92
  5. 2024GFT: Graph Foundation Model with Transferable Tree Vocabularycited by 85
  6. 2021Towards Self-Explainable Graph Neural Networkcited by 75
  7. 2021Lorentzian Graph Convolutional Networkscited by 68
  8. 2022A Survey on Graph Representation Learning Methodscited by 66
  9. 2021GraphMI: Extracting Private Graph Data from Graph Neural Networkscited by 50
  10. 2021On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphscited by 49
  11. 2021Single Node Injection Attack against Graph Neural Networkscited by 45
  12. 2022Non-IID Transfer Learning on Graphscited by 43
38 more
  1. 2022Spectral Augmentation for Self-Supervised Learning on Graphscited by 39
  2. 2024Fully-inductive Node Classification on Arbitrary Graphscited by 39
  3. 2021Rethinking Graph Auto-Encoder Models for Attributed Graph Clusteringcited by 37
  4. 2025RiemannGFM: Learning a Graph Foundation Model from Riemannian Geometrycited by 35
  5. 2023GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learningcited by 33
  6. 2022Graph Auto-Encoder Via Neighborhood Wasserstein Reconstructioncited by 28
  7. 2021Graph Pointer Neural Networkscited by 27
  8. 2021Learning Conjoint Attentions for Graph Neural Netscited by 26
  9. 2022Model Inversion Attacks against Graph Neural Networkscited by 26
  10. 2021Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspectivecited by 23
  11. 2021Network Representation Learning: From Preprocessing, Feature Extraction to Node Embeddingcited by 19
  12. 2022Geodesic Graph Neural Network for Efficient Graph Representation Learningcited by 19
  13. 2024Can LLMs Convert Graphs to Text-Attributed Graphs?cited by 19
  14. 2025On the Benefits of Attribute-Driven Graph Domain Adaptationcited by 19
  15. 2021Beyond Low-Pass Filters: Adaptive Feature Propagation on Graphscited by 18
  16. 2021Source Free Unsupervised Graph Domain Adaptationcited by 18
  17. 2023When to Pre-Train Graph Neural Networks? From Data Generation Perspective!cited by 18
  18. 2021Generalizable Cross-Graph Embedding for GNN-based Congestion Predictioncited by 17
  19. 2022A Graph-Enhanced Click Model for Web Searchcited by 17
  20. 2022Node-oriented Spectral Filtering for Graph Neural Networkscited by 17
  21. 2024Subgraph Pooling: Tackling Negative Transfer on Graphscited by 17
  22. 2024A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptationcited by 17
  23. 2025Pave Your Own Path: Graph Gradual Domain Adaptation on Fused Gromov-Wasserstein Geodesicscited by 17
  24. 2025Equivariance Everywhere All At Once: A Recipe for Graph Foundation Modelscited by 17
  25. 2023On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximationcited by 16
  26. 2024Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspectivecited by 16
  27. 2025Bringing Graphs to the Table: Zero-shot Node Classification via Tabular Foundation Modelscited by 16
  28. 2022Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Coloringscited by 15
  29. 2022A Survey on Influence Maximization: From an ML-Based Combinatorial Optimizationcited by 15
  30. 2023A survey and taxonomy of loss functions in machine learningcited by 15
  31. 2022Meta-Weight Graph Neural Network: Push the Limits Beyond Global Homophilycited by 14
  32. 2022Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applicationscited by 14
  33. 2023Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similaritycited by 14
  34. 2023Better with Less: A Data-Active Perspective on Pre-Training Graph Neural Networkscited by 14
  35. 2024A Survey of Generative Techniques for Spatial-Temporal Data Miningcited by 14
  36. 2023Pitfalls in Link Prediction with Graph Neural Networks: Understanding the Impact of Target-link Inclusion & Better Practicescited by 13
  37. 2023MUSE: Multi-View Contrastive Learning for Heterophilic Graphscited by 13
  38. 2024A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directionscited by 13

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