Quantifying randomness in real networks.
- Orsini C CAIDA, University of California San Diego, San Diego, California 92093, USA.
- Dankulov MM Scientific Computing Laboratory, Institute of Physics Belgrade, University of Belgrade, Belgrade 11080, Serbia.
- Colomer-de-Simón P Departament de Física Fonamental, Universitat de Barcelona, Barcelona 08028, Spain.
- Jamakovic A Communication and Distributed Systems group, Institute of Computer Science and Applied Mathematics, University of Bern, Bern 3012, Switzerland.
- Mahadevan P Palo Alto Research Center, Palo Alto, California 94304, USA.
- Vahdat A Google, Mountain View, California 94043, USA.
- Bassler KE Department of Physics and Texas Center for Superconductivity, University of Houston, Houston, Texas 77204, USA.
- Toroczkai Z Department of Physics and Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, IN 46556, USA.
- Boguñá M Departament de Física Fonamental, Universitat de Barcelona, Barcelona 08028, Spain.
- Caldarelli G IMT Alti Studi, Lucca 55100, Italy.
- Fortunato S Department of Computer Science, Aalto University School of Science, Helsinki 00076, Finland.
- Krioukov D CAIDA, University of California San Diego, San Diego, California 92093, USA.
- 2015-10-21
Published in:
- Nature communications. - 2015
English
Represented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the dk-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks--the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain--and find that many important local and global structural properties of these networks are closely reproduced by dk-random graphs whose degree distributions, degree correlations and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation of network randomness, and release software to generate dk-random graphs.
- Language
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- English
- Open access status
- gold
- Identifiers
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- DOI 10.1038/ncomms9627
- PMID 26482121
- Persistent URL
- https://sonar.ch/global/documents/170066
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