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• W2900800369 abstract "We consider the exact recovery problem in the hypergraph stochastic block model (HSBM) with k blocks of equal size. More precisely, we consider a random d-uniform hypergraph H with n vertices partitioned into k clusters of size s=n/k. Hyperedges e are added independently with probability p if e is contained within a single cluster and q otherwise, where 0≤q<p≤1. We present a spectral algorithm which recovers the clusters exactly with high probability, given mild conditions on n,k,p,q, and d. Our algorithm is based on the adjacency matrix of H, which is a symmetric n×n matrix whose (u,v)-th entry is the number of hyperedges containing both u and v. To the best of our knowledge, our algorithm is the first to guarantee exact recovery when the number of clusters k=Θ(n)." @default.
• W2900800369 created "2018-11-29" @default.
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• W2900800369 date "2020-05-01" @default.
• W2900800369 modified "2023-10-16" @default.
• W2900800369 title "Exact recovery in the hypergraph stochastic block model: A spectral algorithm" @default.
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• W2900800369 doi "https://doi.org/10.1016/j.laa.2020.01.039" @default.
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