{
  "id": "1904.05981",
  "version": "v1",
  "published": "2019-04-11T23:23:21.000Z",
  "updated": "2019-04-11T23:23:21.000Z",
  "title": "Community Detection in the Sparse Hypergraph Stochastic Block Model",
  "authors": [
    "Soumik Pal",
    "Yizhe Zhu"
  ],
  "comment": "46 pages, 4 figures",
  "categories": [
    "math.PR",
    "cs.LG",
    "math.CO",
    "stat.ML"
  ],
  "abstract": "We consider the community detection problem in sparse random hypergraphs. Angelini et al. (2015) conjectured the existence of a sharp threshold on model parameters for community detection in sparse hypergraphs generated by a hypergraph stochastic block model (HSBM). We solve the positive part of the conjecture for the case of two blocks: above the threshold, there is a spectral algorithm which asymptotically almost surely constructs a partition of the hypergraph correlated with the true partition. Our method is a generalization to random hypergraphs of the method developed by Massouli\\'{e} (2014) for sparse random graphs.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-04-11T23:23:21.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "sparse hypergraph stochastic block model",
      "sparse random hypergraphs",
      "community detection problem",
      "sparse random graphs"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 46,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}