Mesoscopic analysis of networks: applications to exploratory analysis and data clustering
arXiv:1101.1881 · doi:10.1063/1.3560932
Abstract
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.
Chaos (2011) in press
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- Detecting community structure in networks using edge prediction methods
- Community structure revealed by phase locking
- Structural patterns in complex systems using multidendrograms