COMMUNITY DETECTION IN DENSE RANDOM NETWORKS

被引:84
作者
Arias-Castro, Ery [1 ]
Verzelen, Nicolas [2 ]
机构
[1] Univ Calif San Diego, San Diego, CA 92103 USA
[2] INRA, UMR MISTEA 729, F-34060 Montpellier, France
关键词
Community detection; detecting a dense subgraph; minimax hypothesis testing; Erdos-Renyi random graph; scan statistic; planted clique problem; dense k-subgraph problem; sparse eigenvalue problem; ANOMALY DETECTION; HIGHER CRITICISM; SPARSE;
D O I
10.1214/14-AOS1208
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
We formalize the problem of detecting a community in a network into testing whether in a given (random) graph there is a subgraph that is unusually dense. Specifically, we observe an undirected and unweighted graph on N nodes. Under the null hypothesis, the graph is a realization of an Erdos-Renyi graph with probability p(0). Under the (composite) alternative, there is an unknown subgraph of n nodes where the probability of connection is P-1 > p(0). We derive a detection lower bound for detecting such a subgraph in terms of N, n, p(0), P1 and exhibit a test that achieves that lower bound. We do this both when p(0) is known and unknown. We also consider the problem of testing in polynomial-time. As an aside, we consider the problem of detecting a clique, which is intimately related to the planted clique problem. Our focus in this paper is in the quasi-normal regime where np(0) is either bounded away from zero, or tends to zero slowly.
引用
收藏
页码:940 / 969
页数:30
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