Graph clustering using multiway ratio cut (Software demonstration)

Tom Roxborough, Arunabha Sen

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

16 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 ratio cut, a well known technique used for circuit partitioning in the VLSI domain. The algorithm is implemented in WINDOWS95/NT environment. 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
Pages291-296
Number of pages6
Volume1353
ISBN (Print)9783540639381
DOIs
StatePublished - 1997
Event5th International Symposium on Graph Drawing, GD 1997 - Rome, Italy
Duration: Sep 18 1997Sep 20 1997

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1353
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other5th International Symposium on Graph Drawing, GD 1997
CountryItaly
CityRome
Period9/18/979/20/97

    Fingerprint

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Roxborough, T., & Sen, A. (1997). Graph clustering using multiway ratio cut (Software demonstration). In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1353, pp. 291-296). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1353). Springer Verlag. https://doi.org/10.1007/3-540-63938-1_71