Information Theoretic Limits of Exact Recovery in Sub-hypergraph Models for Community Detection
This work provides foundational limits for community detection in hypergraph models, which is incremental but important for theoretical understanding in network analysis.
The paper tackles the problem of exact recovery in community detection using sub-hypergraph models, establishing tight information-theoretic bounds that define regions where algorithms fail or succeed with high probability.
In this paper, we study the information theoretic bounds for exact recovery in sub-hypergraph models for community detection. We define a general model called the $m-$uniform sub-hypergraph stochastic block model ($m-$ShSBM). Under the $m-$ShSBM, we use Fano's inequality to identify the region of model parameters where any algorithm fails to exactly recover the planted communities with a large probability. We also identify the region where a Maximum Likelihood Estimation (MLE) algorithm succeeds to exactly recover the communities with high probability. Our bounds are tight and pertain to the community detection problems in various models such as the planted hypergraph stochastic block model, the planted densest sub-hypergraph model, and the planted multipartite hypergraph model.