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

Details Mesoscopic analysis of networks: applications to exploratory analysis and data clustering Mesoscopic analysis of networks: applications to exploratory analysis and data clustering

2011-01-10

arxiv.org/abs/1101.1881v2

Chaos 21 (2011) 016102

Physics

Data Analysis, Statistics and Probability

Chaos (2011) in press

Scientific paper

10.1063/1.3560932

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 multi-resolution 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 {\em Caenorhabditis elegans}, and to automatically classify a typical benchmark of unsupervised clustering, the Iris data set, with considerable success.

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