Graph clustering using distance-k cliques software demonstration

Jubin Edachery, Arunabha Sen, Franz J. Brandenburg

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

34 Scopus citations

Abstract

Identifying the natural clusters of nodes in a graph and treating them as supernodes or metanodes for a higher level graph (or an abstract graph) is a technique used for the reduction of visual complexity of graphs with a large number of nodes. In this paper we report on the implementation of a clustering algorithm based on the idea of distance-k cliques, a generalization of the idea of the cliques in graphs. The performance of the clustering algorithm on some large graphs obtained from the archives of Bell Laboratories is presented.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages98-106
Number of pages9
Volume1731
ISBN (Print)3540669043, 9783540669043
DOIs
StatePublished - 1999
Event7th International Symposium on Graph Drawing, GD 1999 - Prague, Czech Republic
Duration: Sep 15 1999Sep 19 1999

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1731
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other7th International Symposium on Graph Drawing, GD 1999
CountryCzech Republic
CityPrague
Period9/15/999/19/99

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Edachery, J., Sen, A., & Brandenburg, F. J. (1999). Graph clustering using distance-k cliques software demonstration. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1731, pp. 98-106). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1731). Springer Verlag. https://doi.org/10.1007/3-540-46648-7_10