Extracting the multiscale backbone of complex weighted networks

By M. Ángeles Serrano, Marián Boguñá, Alessandro Vespignani. Proceedings of the National Academy of Sciences of the United States of America, 2009

882 citations recorded by the source corpus.

A large number of complex systems find a natural abstraction in the form of weighted networks whose nodes represent the elements of the system and the weighted edges identify the presence of an interaction and its relative strength. In recent years, the study of an increasing number of large-scale networks has highlighted the statistical heterogeneity of their interaction pattern, with degree and weight distributions that vary over many orders of magnitude. These features, along with the large number of elements and links, make the extraction of the truly relevant connections forming the network's backbone a very challenging problem. More specifically, coarse-graining approaches and filtering techniques come into conflict with the multiscale nature of large-scale systems. Here, we define a filtering method that offers a practical procedure to extract the relevant connection backbone in complex multiscale networks, preserving the edges that represent statistically significant deviations with respect to a null model for the local assignment of weights to edges. An important aspect of the method is that it does not belittle small-scale interactions and operates at all scales defined by the weight distribution. We apply our method to real-world network instances and compare the obtained results with alternative backbone extraction techniques.

Subfields: Computational Theory and Mathematics, Geometry and Topology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/96c8e0ad23dab088ca746e0f15b09d167784807d

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Citations in the corpus

Fake news on Twitter during the 2016 U.S. presidential election (2019, background): “Essentially, network patterns dictated by high density and differences in popularity tend to obscure the multi-scale nature of network structure (63).”
The statistical physics of real-world networks (2018, background): “The problem of building a statistically validated projection of a bipartite network is similar in spirit to that of extracting the backbone from standard weighted networks [80–83], and has been typically addressed by determining which links are significant using a threshold— either unconditional or ”
Disrupted Modularity and Local Connectivity of Brain Functional Networks in Childhood-Onset Schizophrenia (2010, neutral): “…in Systems Neuroscience www.frontiersin.org October 2010 | Volume 4 | Article 147 | 13 global threshold to a correlation matrix have previously been documented in physics and economics, and several alternatives have been proposed (Onnela et al., 2002; Tumminello et al., 2005; Serrano et al., 2009).”