Condition numbers and scale free graphs
Disordered Systems and Neural Networks
2016-08-16 v1
Abstract
In this work we study the condition number of the least square matrix corresponding to scale free networks. We compute a theoretical lower bound of the condition number which proves that they are ill conditioned. Also, we analyze several matrices from networks generated with the linear preferential attachment model showing that it is very difficult to compute the power law exponent by the least square method due to the severe lost of accuracy expected from the corresponding condition numbers.
Cite
@article{arxiv.cond-mat/0602276,
title = {Condition numbers and scale free graphs},
author = {Gabriel Acosta and Matías Graña and Juan Pablo Pinasco},
journal= {arXiv preprint arXiv:cond-mat/0602276},
year = {2016}
}
Comments
Submitted to EPJ