Self-similarity of complex networks

Chaoming Song, Shlomo Havlin, Hernán A. Makse

Research output: Contribution to journalArticlepeer-review

1149 Scopus citations

Abstract

Complex networks have been studied extensively owing to their relevance to many real systems such as the world-wide web, the Internet, energy landscapes and biological and social networks. A large number of real networks are referred to as 'scale-free' because they show a power-law distribution of the number of links per node. However, it is widely believed that complex networks are not invariant or self-similar under a length-scale transformation. This conclusion originates from the 'small-world' property of these networks, which implies that the number of nodes increases exponentially with the 'diameter' of the network, rather than the power-law relation expected for a self-similar structure. Here we analyse a variety of real complex networks and find that, on the contrary, they consist of self-repeating patterns on all length scales. This result is achieved by the application of a renormalization procedure that coarse-grains the system into boxes containing nodes within a given 'size'. We identify a power-law relation between the number of boxes needed to cover the network and the size of the box, defining a finite self-similar exponent. These fundamental properties help to explain the scale-free nature of complex networks and suggest a common self-organization dynamics.

Original languageEnglish
Pages (from-to)392-395
Number of pages4
JournalNature
Volume433
Issue number7024
DOIs
StatePublished - 27 Jan 2005

Bibliographical note

Funding Information:
Acknowledgements We are grateful to J. Brujić for many discussions. This work is supported by the National Science Foundation, Materials Theory. S.H. thanks the Israel Science Foundation and ONR for support.

Funding

Acknowledgements We are grateful to J. Brujić for many discussions. This work is supported by the National Science Foundation, Materials Theory. S.H. thanks the Israel Science Foundation and ONR for support.

FundersFunder number
National Science Foundation
Office of Naval Research
Israel Science Foundation

    Fingerprint

    Dive into the research topics of 'Self-similarity of complex networks'. Together they form a unique fingerprint.

    Cite this