Research map / Graph learning / Graph algorithms and clustering
Contextual Stochastic Block Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Combine graph edges and node covariates for inference | Contextual SBM · Sparse contextual SBM, large-degree limit | weak recovery threshold: λ^2+μ^2/γ>1 | Helped |
| Use graph edges alone instead of combining edges and covariates | Contextual SBM · Sparse stochastic block model, no covariates | weak recovery threshold: λ > 1 | Hurt |
| Use covariates alone instead of combining covariates and graph edges | Contextual SBM · Spiked covariance model, no graph | weak recovery threshold: μ > √γ | Hurt |
| Run belief propagation on graph and covariate observations | Contextual SBM · Simulated contextual SBM | community-label overlap | Helped |
Closest papers by meaning
- Correlated Stochastic Block Models: Exact Graph Matching with Applications to Recovering Communities (NeurIPS 2021 · similarity 0.92)
- Harnessing Multiple Correlated Networks for Exact Community Recovery (NeurIPS 2024 · similarity 0.91)
- Information-theoretic Limits for Community Detection in Network Models (NeurIPS 2018 · similarity 0.90)
- An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees (ICML 2022 · similarity 0.90)
- Efficient Graph Matching for Correlated Stochastic Block Models (NeurIPS 2024 · similarity 0.88)
- Exact Community Recovery under Side Information: Optimality of Spectral Algorithms (ICLR 2025 · similarity 0.87)
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