Characterizing the structural complexity of real-world complex networks

Research output: Chapter in Book/Report/Conference proceedingsChapterpeer-review

Abstract

Although recent research has shown that the complexity of a network depends on its structural organization, which is linked to the functional constraints the network must satisfy, there is still no systematic study on how to distinguish topological structure and measure the corresponding structural complexity of complex networks. In this paper, we propose the first consistent framework for distinguishing and measuring the structural complexity of real-world complex networks. In terms of the smallest d of the dK model with high-order constraints necessary for fitting real networks, we can classify real-world networks into different structural complexity levels. We demonstrate the approach by measuring and classifying a variety of real-world networks, including biological and technological networks, small-world and non-small-world networks, and spatial and non-spatial networks.

Original languageEnglish
Title of host publicationComplex Sciences - First International Conference, Complex 2009, Revised Papers
Pages1178-1189
Number of pages12
EditionPART 1
DOIs
Publication statusPublished - 2009
Event1st International Conference on Complex Sciences: Theory and Applications, Complex 2009 - Shanghai, China
Duration: 23 Feb 200925 Feb 2009

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering
NumberPART 1
Volume4 LNICST
ISSN (Print)1867-8211

Conference

Conference1st International Conference on Complex Sciences: Theory and Applications, Complex 2009
Country/TerritoryChina
CityShanghai
Period23/02/0925/02/09

Keywords

  • Complex networks
  • Random graph generators
  • Structural complexity

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