Community detection in the sparse hypergraph stochastic block model
Community detection in the sparse hypergraph stochastic block model
复制标题
DOI：
10.1002/rsa.21006
复制
发表时间：
2021-03-14
影响因子：
1
通讯作者：
Zhu, Yizhe
中科院分区：
文献类型：
--
作者：
Pal, Soumik;Zhu, Yizhe
我们考虑稀疏随机超图中的社区检测问题。安杰利尼等人。在[6]中推测超图随机块模型生成的稀疏超图中社区检测的模型参数存在尖锐阈值。我们针对两个块的情况求解猜想的正部分：在阈值之上，存在一种谱算法，该算法几乎肯定渐近地构造与真实分区相关的超图分区。我们的方法是 Massoulie (2014) 为稀疏随机图开发的方法的随机超图的推广。
We consider the community detection problem in sparse random hypergraphs. Angelini et al. in [6] conjectured the existence of a sharp threshold on model parameters for community detection in sparse hypergraphs generated by a hypergraph stochastic block model. 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 Massoulie (2014) for sparse random graphs.