Spectral Clustering on Spherical Coordinates Under the Degree-Corrected Stochastic Blockmodel
Spectral Clustering on Spherical Coordinates Under the Degree-Corrected Stochastic Blockmodel
复制标题
度校正随机块模型下球坐标上的谱聚类
DOI：
10.1080/00401706.2021.2008503
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发表时间：
2022
期刊：
影响因子：
2.5
通讯作者：
Passino F
中科院分区：
文献类型：
--
作者：
Passino F
谱聚类是网络图中社区检测的一种流行方法：从图的矩阵表示开始，将节点聚类到从矩阵的截断谱分解获得的低维投影上。正确估计谱聚类算法中的社区数和缩减后的潜在空间维数是保证算法性能的关键。此外，许多现实世界的图，如在网络安全应用中研究的企业计算机网络，经常显示异构的社区内度分布。这样的异构度分布通常不能很好地捕捉标准谱聚类算法。在本文中，提出了一种新的谱聚类算法的度校正随机块模型下的社区检测。该方法是基于一个变换的谱嵌入到球坐标系中，和一个新的建模假设在变换空间。该方法允许同时和自动选择社区的数量和潜在的维度谱嵌入的图与不均匀的节点度。结果表明，在代表计算机网络的竞争方法的性能有所改善。
Spectral clustering is a popular method for community detection in network graphs: starting from a matrix representation of the graph, the nodes are clustered on a low-dimensional projection obtained from a truncated spectral decomposition of the matrix. Estimating correctly the number of communities and the dimension of the reduced latent space is critical for good performance of spectral clustering algorithms. Furthermore, many real-world graphs, such as enterprise computer networks studied in cyber-security applications, often display heterogeneous within-community degree distributions. Such heterogeneous degree distributions are usually not well captured by standard spectral clustering algorithms. In this article, a novel spectral clustering algorithm is proposed for community detection under the degree-corrected stochastic blockmodel. The proposed method is based on a transformation of the spectral embedding to spherical coordinates, and a novel modeling assumption in the transformed space. The method allows for simultaneous and automated selection of the number of communities and the latent dimension for spectral embeddings of graphs with uneven node degrees. Results show improved performance over competing methods in representing computer networks.
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DOI：
10.1111/rssb.12509
发表时间：
2017-09
期刊：
Journal of the Royal Statistical Society: Series B (Statistical Methodology)
影响因子：
--
作者：
Patrick Rubin-Delanchy;C. Priebe;M. Tang;Joshua Cape
通讯作者：
Patrick Rubin-Delanchy;C. Priebe;M. Tang;Joshua Cape
使用狄利克雷过程从交互数据中可扩展地推断客户相似性
DOI：
10.1287/mksc.1110.0640
发表时间：
2010
期刊：
Econometrics: Econometric & Statistical Methods - General eJournal
影响因子：
--
作者：
Michael Braun;André Bonfrer
通讯作者：
André Bonfrer
DOI：
10.1111/j.2517-6161.1977.tb01600.x
发表时间：
1977-01-01
期刊：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL
影响因子：
--
作者：
DEMPSTER, AP;LAIRD, NM;RUBIN, DB
通讯作者：
RUBIN, DB
谱图聚类的同时维数和复杂性模型选择
影响因子：
2.4
作者：
Congyuan Yang;C. Priebe;Youngser Park;D. Marchette
通讯作者：
D. Marchette