Extracting unbiased information from complex networks
D. Garlaschelli, and M. Loffredo. Abstract Book of the XXIII IUPAP International Conference on Statistical Physics, Genova, Italy, (9-13 July 2007)
Abstract
Information on the organization of a network is usually obtained in terms of the parameter values of a model reproducing the observed topology. However, the parameter choice is often subjective. Here we propose a novel method, based on the Maximum Likelihood principle, to extract a unique, statistically rigorous parameter value from topological data. In this framework, network models turn out to be in general ill-defined or biased; therefore we show a way to define a class of unbiased models. Remarkably, our approach can also be extended in order to extract, only from topological data, the `hidden variables' underlying network organization, making them `no more hidden'. It also solves the problem to correctly randomize a real-world network keeping some of its properties fixed, and allows one to compute averages over the randomized ensemble analytically.
%0 Book Section
%1 statphys23_0989
%A Garlaschelli, D.
%A Loffredo, M.I.
%B Abstract Book of the XXIII IUPAP International Conference on Statistical Physics
%C Genova, Italy
%D 2007
%E Pietronero, Luciano
%E Loreto, Vittorio
%E Zapperi, Stefano
%K likelihood maximum mechanics methods networks statistical statphys23 topic-11
%T Extracting unbiased information from complex networks
%U http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=989
%X Information on the organization of a network is usually obtained in terms of the parameter values of a model reproducing the observed topology. However, the parameter choice is often subjective. Here we propose a novel method, based on the Maximum Likelihood principle, to extract a unique, statistically rigorous parameter value from topological data. In this framework, network models turn out to be in general ill-defined or biased; therefore we show a way to define a class of unbiased models. Remarkably, our approach can also be extended in order to extract, only from topological data, the `hidden variables' underlying network organization, making them `no more hidden'. It also solves the problem to correctly randomize a real-world network keeping some of its properties fixed, and allows one to compute averages over the randomized ensemble analytically.
@incollection{statphys23_0989,
abstract = {Information on the organization of a network is usually obtained in terms of the parameter values of a model reproducing the observed topology. However, the parameter choice is often subjective. Here we propose a novel method, based on the Maximum Likelihood principle, to extract a unique, statistically rigorous parameter value from topological data. In this framework, network models turn out to be in general ill-defined or biased; therefore we show a way to define a class of unbiased models. Remarkably, our approach can also be extended in order to extract, only from topological data, the `hidden variables' underlying network organization, making them `no more hidden'. It also solves the problem to correctly randomize a real-world network keeping some of its properties fixed, and allows one to compute averages over the randomized ensemble analytically.},
added-at = {2007-06-20T10:16:09.000+0200},
address = {Genova, Italy},
author = {Garlaschelli, D. and Loffredo, M.I.},
biburl = {https://www.bibsonomy.org/bibtex/273a2d19a076baad3163a15f2f209f12b/statphys23},
booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics},
editor = {Pietronero, Luciano and Loreto, Vittorio and Zapperi, Stefano},
interhash = {8b6efec7a35762970979f0920ba50690},
intrahash = {73a2d19a076baad3163a15f2f209f12b},
keywords = {likelihood maximum mechanics methods networks statistical statphys23 topic-11},
month = {9-13 July},
timestamp = {2007-06-20T10:16:36.000+0200},
title = {Extracting unbiased information from complex networks},
url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=989},
year = 2007
}