Mesoscopic analysis of networks: Applications to exploratory analysis and data clustering

被引:19
作者
Granell, Clara [1 ]
Gomez, Sergio [1 ]
Arenas, Alex [1 ]
机构
[1] Univ Rovira & Virgili, Dept Engn Informat & Matemat, Tarragona 43007, Catalonia, Spain
关键词
COMMUNITY STRUCTURE; COMPLEX; MODULARITY; SYSTEM;
D O I
10.1063/1.3560932
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
We investigate the adaptation and performance of modularity-based algorithms, designed in the scope of complex networks, to analyze the mesoscopic structure of correlation matrices. Using a multiresolution analysis, we are able to describe the structure of the data in terms of clusters at different topological levels. We demonstrate the applicability of our findings in two different scenarios: to analyze the neural connectivity of the nematode Caenorhabditis elegans and to automatically classify a typical benchmark of unsupervised clustering, the Iris dataset, with considerable success. (C) 2011 American Institute of Physics. [doi: 10.1063/1.3560932]
引用
收藏
页数:9
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