Cited by 350Complex Network Analysis TechniquesAdvanced Clustering Algorithms ResearchBioinformatics and Genomic Networks

Robustness of community structure in networks

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Karrer, Brian, Levina, Elizaveta, Newman, M. E. J.·2008·Physical Review E

Abstract

The discovery of community structure is a common challenge in the analysis of network data. Many methods have been proposed for finding community structure, but few have been proposed for determining whether the structure found is statistically significant or whether, conversely, it could have arisen purely as a result of chance. In this paper we show that the significance of community structure can be effectively quantified by measuring its robustness to small perturbations in network structure. We propose a suitable method for perturbing networks and a measure of the resulting change in community structure and use them to assess the significance of community structure in a variety of networks, both real and computer generated.

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  1. [22]T. suppress Luczak, Sparse random graphs with a given degree sequence. In A. M. Frieze and T. suppress Luczak (eds.), Proceedings of the Symposium on Random Graphs, Pozna´ n 1989 , pp. 165-182, John Wiley, New York (1992).
  2. [41]F. Brglez, D. Bryan, and K. Kozminski, Iscas'89 benchmarks. Proc. IEEE Int. Symp. Circuits Syst. pp. 19291934 (1989).

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Authors
Karrer, B., Levina, E., & Newman, M. E. J.
Affiliations
University of Michigan (US)
Venue
Physical Review E
Published
2008-04-29
Volume
77
Issue
4
Pages
046119-046119
ISSN
  • 1539-3755 (ISSN-L)
  • 1550-2376
OpenAlex
W2136088806
Cited by
350
References
47
Citations by year
2026: 8 · 2025: 11 · 2024: 5 · 2023: 4 · 2022: 9 · 2021: 21 · 2020: 18 · 2019: 23 · 2018: 15 · 2017: 12 · 2016: 19 · 2015: 21 · 2014: 34 · 2013: 31 · 2012: 32
FWCI
21.90
Type
journal article
Language
en
Open access
Locations
  • Physical Review E (publishedVersion)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
community structure, network robustness, perturbation analysis, network perturbation, community detection

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