CONCENTRATION OF RANDOM GRAPHS AND APPLICATION TO COMMUNITY DETECTION
CONCENTRATION OF RANDOM GRAPHS AND APPLICATION TO COMMUNITY DETECTION
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随机图的集中及其在社区检测中的应用
DOI：
10.1142/9789813272880_0166
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
R. Vershynin
中科院分区：
文献类型：
--
作者：
Can M. Le;E. Levina;R. Vershynin
随机矩阵理论在最近的统计网络分析工作中发挥了重要作用。本文回顾了最近关于随机图在其期望值附近的集中度的研究结果，证明了稠密图和稀疏图在正则化后的集中度。我们还回顾了相关的网络模型，可能感兴趣的概率考虑新的随机矩阵理论的发展方向，和随机矩阵理论的工具，可能感兴趣的统计学家希望证明网络算法的属性。详细讨论了集中度结果在网络社区发现问题中的应用。
Random matrix theory has played an important role in recent work on statistical network analysis. In this paper, we review recent results on regimes of concentration of random graphs around their expectation, showing that dense graphs concentrate and sparse graphs concentrate after regularization. We also review relevant network models that may be of interest to probabilists considering directions for new random matrix theory developments, and random matrix theory tools that may be of interest to statisticians looking to prove properties of network algorithms. Applications of concentration results to the problem of community detection in networks are discussed in detail.
影响因子：
2.7
作者：
Li, Tianxi;Levina, Elizaveta;Zhu, Ji
通讯作者：
Zhu, Ji