摘要
社区检测是网络数据统计分析的核心问题之一.本文研究了在非对称稀疏二部分随机块模型中社区结构能否达成精确恢复的条件,主要表现为在极大似然方法下给出了一个仅与相对稠密社区内部连边概率和两社区间的连边概率有关的阈值.此外,我们通过谱聚类方法进行了一系列数据模拟试验,模拟结果很好地验证了本文的结论.
Community detection is one of the core issues in the statistical analysis of network data.In this paper,we study a sufficient condition under which the community structure can be exactly recovered with high probability in the sparse asymmetric planted bisection models.Using the idea of the maximum likelihood method,we obtain a threshold,which is only related to the probabilities of edge presence in the denser community and between communities,for the community detection in the proposed model.In addition,we conduct a series of simulation studies to demonstrate our theoretical results with the spectral clustering method.
作者
赵涛
冯群强
ZHAO Tao;FENG Qunqiang(Department of Statistics and Finance,School of Management,University of Science and Technology of China,Hefei,230026,China)
出处
《应用概率统计》
北大核心
2025年第1期136-151,共16页
Chinese Journal of Applied Probability and Statistics
基金
国家自然科学资金项目(批准号:11771418)资助.
关键词
统计网络模型
社区结构
社区检测
精确恢复
statistical network model
community structure
community detection
exact recovery
作者简介
通讯作者:冯群强,E-mail:fengqq@ustc.edu.cn。
