mlsanityData to 14 Oct 2026

Multi-scale Attributed Node Embedding

arXiv 1909.13021 ↗First posted 28 Sep 2019cs.LG cs.NI cs.SIcited by 548

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Abstract (arXiv)

We present network embedding algorithms that capture information about a node from the local distribution over node attributes around it, as observed over random walks following an approach similar to Skip-gram. Observations from neighborhoods of different sizes are either pooled (AE) or encoded distinctly in a multi-scale approach (MUSAE). Capturing attribute-neighborhood relationships over multiple scales is useful for a diverse range of applications, including latent feature identification across disconnected networks with similar attributes. We prove theoretically that matrices of node-feature pointwise mutual information are implicitly factorized by the embeddings. Experiments show that our algorithms are robust, computationally efficient and outperform comparable models on social networks and web graphs.

Key facts. Cited by 548 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Sep 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 57; 2022: 97; 2023: 103; 2024: 125; 2025: 101; 2026: 65.

548 papers.

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  1. 2021Understanding over-squashing and bottlenecks on graphs via curvaturecited by 478
  2. 2021Beyond Low-frequency Information in Graph Convolutional Networkscited by 362
  3. 2023A critical look at the evaluation of GNNs under heterophily: Are we really making progress?cited by 343
  4. 2021Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodscited by 311
  5. 2022Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNscited by 214
  6. 2021BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximationcited by 195
  7. 2022Pure Transformers are Powerful Graph Learnerscited by 188
  8. 2021Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networkscited by 182
  9. 2021Is Homophily a Necessity for Graph Neural Networks?cited by 182
  10. 2022Graph Neural Networks for Graphs with Heterophily: A Surveycited by 175
  11. 2022How Powerful are Spectral Graph Neural Networkscited by 169
  12. 2022Revisiting Heterophily For Graph Neural Networkscited by 169
38 more
  1. 2022Handling Distribution Shifts on Graphs: An Invariance Perspectivecited by 164
  2. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 138
  3. 2023SGFormer: Simplifying and Empowering Transformers for Large-Graph Representationscited by 133
  4. 2021MagNet: A Neural Network for Directed Graphscited by 107
  5. 2021PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equationscited by 106
  6. 2022How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervisioncited by 102
  7. 2023Specformer: Spectral Graph Neural Networks Meet Transformerscited by 102
  8. 2022Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvaturecited by 98
  9. 2022Out-Of-Distribution Generalization on Graphs: A Surveycited by 97
  10. 2021Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patternscited by 95
  11. 2022Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networkscited by 94
  12. 2022Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisitedcited by 87
  13. 2024Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classificationcited by 85
  14. 2021GBK-GNN: Gated Bi-Kernel Graph Neural Networks for Modeling Both Homophily and Heterophilycited by 84
  15. 2023Attending to Graph Transformerscited by 83
  16. 2021New Benchmarks for Learning on Non-Homophilous Graphscited by 81
  17. 2022Empowering Graph Representation Learning with Test-Time Graph Transformationcited by 67
  18. 2023Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothingcited by 67
  19. 2021LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysiscited by 65
  20. 2022Sheaf Neural Networks with Connection Laplacianscited by 65
  21. 2022On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networkscited by 64
  22. 2025SAMGPT: Text-free Graph Foundation Model for Multi-domain Pre-training and Cross-domain Adaptationcited by 62
  23. 2022Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyondcited by 60
  24. 2022Understanding convolution on graphs via energiescited by 59
  25. 2022GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networkscited by 58
  26. 2021Simplifying approach to Node Classification in Graph Neural Networkscited by 56
  27. 2022GREAD: Graph Neural Reaction-Diffusion Networkscited by 54
  28. 2021Local Augmentation for Graph Neural Networkscited by 53
  29. 2022KerGNNs: Interpretable Graph Neural Networks with Graph Kernelscited by 50
  30. 2021Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddingscited by 49
  31. 2022Gradient Gating for Deep Multi-Rate Learning on Graphscited by 49
  32. 2023A Fractional Graph Laplacian Approach to Oversmoothingcited by 45
  33. 2021Boost then Convolve: Gradient Boosting Meets Graph Neural Networkscited by 44
  34. 2021Improving Graph Neural Networks with Simple Architecture Designcited by 41
  35. 2022TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classificationcited by 41
  36. 2023Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?cited by 41
  37. 2023Mitigating Over-Smoothing and Over-Squashing using Augmentations of Forman-Ricci Curvaturecited by 41
  38. 2021Scalable Rule-Based Representation Learning for Interpretable Classificationcited by 40

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