%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 }