Extracting the Multiscale Backbone of Complex Weighted Networks

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 n...

Full description

Saved in:
Bibliographic Details
Published inProceedings of the National Academy of Sciences - PNAS Vol. 106; no. 16; pp. 6483 - 6488
Main Authors Serrano, M. Ángeles, Boguñá, Marián, Vespignani, Alessandro, Bickel, Peter J.
Format Journal Article
LanguageEnglish
Published United States National Academy of Sciences 21.04.2009
National Acad Sciences
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary: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 realworld network instances and compare the obtained results with alternative backbone extraction techniques.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Edited by Peter J. Bickel, University of California, Berkeley, CA, and approved March 2, 2009
Author contributions: M.A.S., M.B., and A.V. designed research, performed research, contributed new reagents/analytic tools, analyzed data, and wrote the paper.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.0808904106