mlsanityData to 14 Oct 2026

Contextual Stochastic Block Models

arXiv 1807.09596 ↗First posted 23 Jul 2018cs.SI cs.LG stat.MLcited by 134

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Abstract (arXiv)

We provide the first information theoretic tight analysis for inference of latent community structure given a sparse graph along with high dimensional node covariates, correlated with the same latent communities. Our work bridges recent theoretical breakthroughs in the detection of latent community structure without nodes covariates and a large body of empirical work using diverse heuristics for combining node covariates with graphs for inference. The tightness of our analysis implies in particular, the information theoretical necessity of combining the different sources of information. Our analysis holds for networks of large degrees as well as for a Gaussian version of the model.

Key facts. Cited by 134 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Jul 2018.

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Citing papers per year: 2021: 12; 2022: 23; 2023: 23; 2024: 37; 2025: 20; 2026: 19.

134 papers.

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  1. 2021Is Homophily a Necessity for Graph Neural Networks?cited by 182
  2. 2021Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Predictioncited by 138
  3. 2021You are AllSet: A Multiset Function Framework for Hypergraph Neural Networkscited by 127
  4. 2021New Benchmarks for Learning on Non-Homophilous Graphscited by 81
  5. 2021Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalizationcited by 70
  6. 2021Subgroup Generalization and Fairness of Graph Neural Networkscited by 69
  7. 2021On Provable Benefits of Depth in Training Graph Convolutional Networkscited by 58
  8. 2022GREAD: Graph Neural Reaction-Diffusion Networkscited by 54
  9. 2022A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networkscited by 49
  10. 2022Equivariant Hypergraph Diffusion Neural Operatorscited by 47
  11. 2023Structural Re-weighting Improves Graph Domain Adaptationcited by 45
  12. 2022FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learningcited by 43
38 more
  1. 2021p-Laplacian Based Graph Neural Networkscited by 39
  2. 2023Sheaf Hypergraph Networkscited by 34
  3. 2023OpenGSL: A Comprehensive Benchmark for Graph Structure Learningcited by 30
  4. 2023Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directionscited by 28
  5. 2022EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networkscited by 26
  6. 2024Pairwise Alignment Improves Graph Domain Adaptationcited by 25
  7. 2022Graph Attention Retrospectivecited by 24
  8. 2022Effects of Graph Convolutions in Multi-layer Networkscited by 24
  9. 2022Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNscited by 23
  10. 2021Correlated Stochastic Block Models: Exact Graph Matching with Applications to Recovering Communitiescited by 22
  11. 2022Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspectivecited by 19
  12. 2023Revisiting Robustness in Graph Machine Learningcited by 19
  13. 2024Towards Robust Graph Incremental Learning on Evolving Graphscited by 18
  14. 2021A Biased Graph Neural Network Sampler with Near-Optimal Regretcited by 17
  15. 2022Simplified Graph Convolution with Heterophilycited by 17
  16. 2024Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approachcited by 17
  17. 2025Pave Your Own Path: Graph Gradual Domain Adaptation on Fused Gromov-Wasserstein Geodesicscited by 17
  18. 2022Augmentation-Free Graph Contrastive Learning with Performance Guaranteecited by 16
  19. 2024Understanding Heterophily for Graph Neural Networkscited by 15
  20. 2022Single-Pass Contrastive Learning Can Work for Both Homophilic and Heterophilic Graphcited by 14
  21. 2023Explaining and Adapting Graph Conditional Shiftcited by 14
  22. 2022ES-GNN: Generalizing Graph Neural Networks Beyond Homophily with Edge Splittingcited by 13
  23. 2022Homophily modulates double descent generalization in graph convolution networkscited by 13
  24. 2023Optimality of Message-Passing Architectures for Sparse Graphscited by 13
  25. 2022Partition-Based Active Learning for Graph Neural Networkscited by 12
  26. 2023Optimal Inference in Contextual Stochastic Block Modelscited by 12
  27. 2023Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptronscited by 12
  28. 2023Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protectioncited by 12
  29. 2024Uncertainty for Active Learning on Graphscited by 12
  30. 2024Joint Graph Rewiring and Feature Denoising via Spectral Resonancecited by 12
  31. 2021An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guaranteescited by 10
  32. 2023A Metadata-Driven Approach to Understand Graph Neural Networkscited by 10
  33. 2024Matcha: Mitigating Graph Structure Shifts with Test-Time Adaptationcited by 10
  34. 2022Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methodscited by 9
  35. 2024Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspectivecited by 9
  36. 2024Can Modifying Data Address Graph Domain Adaptation?cited by 9
  37. 2021Fundamental limits for rank-one matrix estimation with groupwise heteroskedasticitycited by 8
  38. 2022Exact Community Recovery in Correlated Stochastic Block Modelscited by 8

50 most cited of 134 All 134 (JSON)