文章基本信息

标题：On a two-truths phenomenon in spectral graph clustering
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作者：Carey E. Priebe ; Youngser Park ; Joshua T. Vogelstein 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2019
卷号：116
期号：13
页码：5995-6000
DOI：10.1073/pnas.1814462116
出版社：The National Academy of Sciences of the United States of America
摘要：Clustering is concerned with coherently grouping observations without any explicit concept of true groupings. Spectral graph clustering—clustering the vertices of a graph based on their spectral embedding—is commonly approached via K -means (or, more generally, Gaussian mixture model) clustering composed with either Laplacian spectral embedding (LSE) or adjacency spectral embedding (ASE). Recent theoretical results provide deeper understanding of the problem and solutions and lead us to a “two-truths” LSE vs. ASE spectral graph clustering phenomenon convincingly illustrated here via a diffusion MRI connectome dataset: The different embedding methods yield different clustering results, with LSE capturing left hemisphere/right hemisphere affinity structure and ASE capturing gray matter/white matter core–periphery structure.
关键词：spectral embedding ; spectral clustering ; graph ; network ; connectome