mlsanityData to 7 Oct 2026

Anonymous Walk Embeddings

arXiv 1805.11921 ↗First posted 30 May 2018cs.LG stat.MLcited by 46

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Abstract (arXiv)

The task of representing entire graphs has seen a surge of prominent results, mainly due to learning convolutional neural networks (CNNs) on graph-structured data. While CNNs demonstrate state-of-the-art performance in graph classification task, such methods are supervised and therefore steer away from the original problem of network representation in task-agnostic manner. Here, we coherently propose an approach for embedding entire graphs and show that our feature representations with SVM classifier increase classification accuracy of CNN algorithms and traditional graph kernels. For this we describe a recently discovered graph object, anonymous walk, on which we design task-independent algorithms for learning graph representations in explicit and distributed way. Overall, our work represents a new scalable unsupervised learning of state-of-the-art representations of entire graphs.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 May 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 17; 2022: 14; 2023: 6; 2024: 2; 2025: 5; 2026: 2.

46 papers.

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  1. 2021Inductive Representation Learning in Temporal Networks via Causal Anonymous Walkscited by 188
  2. 2021Federated Graph Classification over Non-IID Graphscited by 113
  3. 2021SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanismcited by 75
  4. 2022A Survey on Graph Representation Learning Methodscited by 66
  5. 2021Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passingcited by 46
  6. 2021A New Perspective on the Effects of Spectrum in Graph Neural Networkscited by 24
  7. 2024Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Miningcited by 20
  8. 2021Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernelscited by 19
  9. 2021Distribution Knowledge Embedding for Graph Poolingcited by 19
  10. 2021Network Representation Learning: From Preprocessing, Feature Extraction to Node Embeddingcited by 19
  11. 2022HAQJSK: Hierarchical-Aligned Quantum Jensen-Shannon Kernels for Graph Classificationcited by 18
  12. 2023Dynamic Graph Representation Learning with Neural Networks: A Surveycited by 15
34 more
  1. 2022A Simple yet Effective Method for Graph Classificationcited by 14
  2. 2021Convergent Graph Solverscited by 11
  3. 2025Generative Graph Pattern Machinecited by 11
  4. 2021Label Contrastive Coding based Graph Neural Network for Graph Classificationcited by 10
  5. 2023HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformerscited by 10
  6. 2021Learning Hyperbolic Representations of Topological Featurescited by 7
  7. 2022A Variational Edge Partition Model for Supervised Graph Representation Learningcited by 6
  8. 2023State of the Art and Potentialities of Graph-level Learningcited by 6
  9. 2021A Survey on Role-Oriented Network Embeddingcited by 4
  10. 2022Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequencescited by 4
  11. 2023Personalized Federated Domain Adaptation for Item-to-Item Recommendationcited by 4
  12. 2021A Literature Review of Recent Graph Embedding Techniques for Biomedical Datacited by 3
  13. 2023Equivariant Matrix Function Neural Networkscited by 3
  14. 2021Temporal Graph Network Embedding with Causal Anonymous Walks Representationscited by 2
  15. 2022Soft-mask: Adaptive Substructure Extractions for Graph Neural Networkscited by 2
  16. 2022Pattern Analysis of Money Flow in the Bitcoin Blockchaincited by 2
  17. 2021Revisiting 2D Convolutional Neural Networks for Graph-based Applicationscited by 1
  18. 2022Representation Learning on Heterostructures via Heterogeneous Anonymous Walkscited by 1
  19. 2022High Performance of Gradient Boosting in Binding Affinity Predictioncited by 1
  20. 2022TPM: Transition Probability Matrix -- Graph Structural Feature based Embeddingcited by 1
  21. 2022Distributed representations of graphs for drug pair scoringcited by 1
  22. 2024PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embeddingcited by 1
  23. 2026End-to-End Subgraph Detection with GraphDETRcited by 1
  24. 2021Structural Optimization Makes Graph Classification Simpler and Bettercited by 0
  25. 2021Capsule Graph Neural Networks with EM Routingcited by 0
  26. 2022Bayesian Deep Learning for Graphscited by 0
  27. 2022Graph-level Neural Networks: Current Progress and Future Directionscited by 0
  28. 2022Metric Distribution to Vector: Constructing Data Representation via Broad-Scale Discrepanciescited by 0
  29. 2023An Evolution Kernel Method for Graph Classification through Heat Diffusion Dynamicscited by 0
  30. 2025ASD Classification on Dynamic Brain Connectome using Temporal Random Walk with Transformer-based Dynamic Network Embeddingcited by 0
  31. 2025ffstruc2vec: Flat, Flexible and Scalable Learning of Node Representations from Structural Identitiescited by 0
  32. 2025Are Graph Attention Networks Able to Model Structural Information?cited by 0
  33. 2025Topologically-Stabilized Graph Neural Networks: Empirical Robustness Across Domainscited by 0
  34. 2026Topology-induced Operators Reveal Complementary Graph Representations without Trainingcited by 0