Beyond the average: detecting global singular nodes from local features in complex networks
Data Analysis, Statistics and Probability
2007-05-23 v1 Disordered Systems and Neural Networks
Physics and Society
Abstract
Deviations from the average can provide valuable insights about the organization of natural systems. This article extends this important principle to the more systematic identification and analysis of singular local connectivity patterns in complex networks. Four measurements quantifying different and complementary features of the connectivity around each node are calculated and multivariate statistical methods are then applied in order to identify outliers. The potential of the presented concepts and methodology is illustrated with respect to a word association network.
Cite
@article{arxiv.physics/0607272,
title = {Beyond the average: detecting global singular nodes from local features in complex networks},
author = {Luciano da F. Costa and Marcus Kaiser and Claus Hilgetag},
journal= {arXiv preprint arXiv:physics/0607272},
year = {2007}
}
Comments
1 figure, 1 table, 5 pages. A working manuscript. Comments very welcomed