ArticleJournal of the American Statistical Association2023
Bias-adjusted spectral clustering in multi-layer stochastic block models.
Article in Journal of the American Statistical Association, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Latent Class Analysis with Arbitrary-Distribution Responses.Entropy (Basel, Switzerland) · 2025Article
- Supervised brain node and network construction under voxel-level functional imaging.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Spectral Clustering, Bayesian Spanning Forest, and Forest Process.Journal of the American Statistical Association · 2024Article
- Article
- Gene communities in co-expression networks across different tissues.PLoS computational biology · 2023Article
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Authors and funding
2 authors.
Funding
Abstract
We consider the problem of estimating common community structures in multi-layer stochastic block models, where each single layer may not have sufficient signal strength to recover the full community structure. In order to efficiently aggregate signal across different layers, we argue that the sum-of-squared adjacency matrices contain sufficient signal even when individual layers are very sparse. Our method uses a bias-removal step that is necessary when the squared noise matrices may overwhelm the signal in the very sparse regime. The analysis of our method relies on several novel tail probability bounds for matrix linear combinations with matrix-valued coefficients and matrix-valued quadratic forms, which may be of independent interest. The performance of our method and the necessity of bias removal is demonstrated in synthetic data and in microarray analysis about gene co-expression networks.
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