二部分随机块模型的精确恢复判别

Exact Recovery Discrimination in Planted Bisection Models

  • 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.

     

    Abstract: 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 or not in the sparse asymmetric planted bisection models. Using 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.

     

/

返回文章
返回