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Analytical maximum-likelihood method to detect patterns in real networks

Squartini, Tiziano and Garlaschelli, Diego Analytical maximum-likelihood method to detect patterns in real networks. New Journal of Physics, 13 (8). 083001. ISSN 1367-2630 (2011)

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In order to detect patterns in real networks, randomized graph ensembles that preserve only part of the topology of an observed network are systematically used as fundamental null models. However, the generation of them is still problematic. Existing approaches are either computationally demanding and beyond analytic control or analytically accessible but highly approximate. Here, we propose a solution to this long-standing problem by introducing a fast method that allows one to obtain expectation values and standard deviations of any topological property analytically, for any binary, weighted, directed or undirected network. Remarkably, the time required to obtain the expectation value of any property analytically across the entire graph ensemble is as short as that required to compute the same property using the adjacency matrix of the single original network. Our method reveals that the null behavior of various correlation properties is different from what was believed previously, and is highly sensitive to the particular network considered. Moreover, our approach shows that important structural properties (such as the modularity used in community detection problems) are currently based on incorrect expressions, and provides the exact quantities that should replace them.

Item Type: Article
Identification Number: 10.1088/1367-2630/13/8/083001
Subjects: Q Science > QC Physics
Research Area: Economics and Institutional Change
Depositing User: Caterina Tangheroni
Date Deposited: 05 Nov 2015 11:00
Last Modified: 08 Mar 2018 17:04
URI: http://eprints.imtlucca.it/id/eprint/2810

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