Maximum likelihood: extracting unbiased information from complex networks
| dc.creator | Garlaschelli, Diego | |
| dc.creator | Loffredo, Maria I. | |
| dc.date | 2006-09-01 | |
| dc.date | 2008-08-07 | |
| dc.date.accessioned | 2026-07-07T09:55:05Z | |
| dc.date.available | 2026-07-07T09:55:05Z | |
| dc.description | The choice of free parameters in network models is subjective, since it depends on what topological properties are being monitored. However, we show that the Maximum Likelihood (ML) principle indicates a unique, statistically rigorous parameter choice, associated to a well defined topological feature. We then find that, if the ML condition is incompatible with the built-in parameter choice, network models turn out to be intrinsically ill-defined or biased. To overcome this problem, we construct a class of safely unbiased models. We also propose an extension of these results that leads to the fascinating possibility to extract, only from topological data, the `hidden variables' underlying network organization, making them `no more hidden'. We test our method on the World Trade Web data, where we recover the empirical Gross Domestic Product using only topological information. | |
| dc.description | Final version accepted for publication | |
| dc.identifier | https://arxiv.org/abs/cond-mat/0609015 | |
| dc.identifier | http://arxiv.org/abs/cond-mat/0609015 | |
| dc.identifier | Phys. Rev. E 78, 015101(R) (2008) | |
| dc.identifier | doi:10.1103/PhysRevE.78.015101 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/166551 | |
| dc.subject | Disordered Systems and Neural Networks | |
| dc.subject | Statistical Mechanics | |
| dc.subject | Data Analysis, Statistics and Probability | |
| dc.subject | Physics and Society | |
| dc.title | Maximum likelihood: extracting unbiased information from complex networks | |
| dc.type | text |