On the separability of structural classes of communities

Bruno Abrahao, Sucheta Soundarajan, John Hopcroft, Robert Kleinberg

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Citations (Scopus)

Abstract

Three major factors govern the intricacies of community extraction in networks: (1) the application domain includes a wide variety of networks of fundamentally different natures, (2) the literature offers a multitude of disparate community detection algorithms, and (3) there is no consensus characterizing how to discriminate communities from non-communities. In this paper, we present a comprehensive analysis of community properties through a class separability framework. Our approach enables the assessement of the structural dissimilarity among the output of multiple community detection algorithms and between the output of algorithms and communities that arise in practice. To demostrate this concept, we furnish our method with a large set of structural properties and multiple community detection algorithms. Applied to a diverse collection of large scale network datasets, the analysis reveals that (1) the different detection algorithms extract fundamentally different structures; (2) the structure of communities that arise in practice is closest to that of communities that random-walk-based algorithms extract, although still siginificantly different from that of the output of all the algorithms; and (3) a small subset of the properties are nearly as discriminative as the full set, while making explicit the ways in which the algorithms produce biases. Our framework enables an informed choice of the most suitable community detection method for a given purpose and network and allows for a comparison of existing community detection algorithms while guiding the design of new ones.

Original languageEnglish (US)
Title of host publicationProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages624-632
Number of pages9
DOIs
StatePublished - 2012
Externally publishedYes
Event18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012 - Beijing, China
Duration: Aug 12 2012Aug 16 2012

Other

Other18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012
CountryChina
CityBeijing
Period8/12/128/16/12

Fingerprint

Set theory
Structural properties

Keywords

  • class separability
  • community structure
  • detection algorithms
  • networks

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Abrahao, B., Soundarajan, S., Hopcroft, J., & Kleinberg, R. (2012). On the separability of structural classes of communities. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 624-632) https://doi.org/10.1145/2339530.2339631

On the separability of structural classes of communities. / Abrahao, Bruno; Soundarajan, Sucheta; Hopcroft, John; Kleinberg, Robert.

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2012. p. 624-632.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abrahao, B, Soundarajan, S, Hopcroft, J & Kleinberg, R 2012, On the separability of structural classes of communities. in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 624-632, 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012, Beijing, China, 8/12/12. https://doi.org/10.1145/2339530.2339631
Abrahao B, Soundarajan S, Hopcroft J, Kleinberg R. On the separability of structural classes of communities. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2012. p. 624-632 https://doi.org/10.1145/2339530.2339631
Abrahao, Bruno ; Soundarajan, Sucheta ; Hopcroft, John ; Kleinberg, Robert. / On the separability of structural classes of communities. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2012. pp. 624-632
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