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Graph clustering

by: Satu Elisa Schaeffer

In: Computer Science Review, Vol. 1, Nr. 1 (2007) , p. 27 - 64.

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URL:	http://www.sciencedirect.com/science/article/B8JDG-4PBG1S7-1/2/6537f3d1ffbf391086c60dbeba874b13
DOI:	10.1016/j.cosrev.2007.05.001

In this survey we overview the definitions and methods for graph clustering, that is, finding sets of "related" vertices in graphs. We review the many definitions for what is a cluster in a graph and measures of cluster quality. Then we present global algorithms for producing a clustering for the entire vertex set of an input graph, after which we discuss the task of identifying a cluster for a specific seed vertex by local computation. Some ideas on the application areas of graph clustering algorithms are given. We also address the problematics of evaluating clusterings and benchmarking cluster algorithms.

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ScienceDirect - Computer Science Review : Graph clustering

BibTeX record

@article{Schaeffer200727,
  abstract = {In this survey we overview the definitions and methods for graph clustering, that is, finding sets of "related" vertices in graphs. We review the many definitions for what is a cluster in a graph and measures of cluster quality. Then we present global algorithms for producing a clustering for the entire vertex set of an input graph, after which we discuss the task of identifying a cluster for a specific seed vertex by local computation. Some ideas on the application areas of graph clustering algorithms are given. We also address the problematics of evaluating clusterings and benchmarking cluster algorithms.},
  added-at = {2010-07-26T19:01:50.000+0200},
  author = {Schaeffer, Satu Elisa},
  biburl = {http://www.bibsonomy.org/bibtex/258ff3e69759d9ec6101b5ee5d91c471c/folke},
  description = {ScienceDirect - Computer Science Review : Graph clustering},
  doi = {10.1016/j.cosrev.2007.05.001},
  interhash = {24c764dba3c31f76ced3aa58f1983ed4},
  intrahash = {58ff3e69759d9ec6101b5ee5d91c471c},
  issn = {1574-0137},
  journal = {Computer Science Review},
  keywords = {clustering community detection graph survey},
  number = 1,
  pages = {27 - 64},
  timestamp = {2010-07-26T19:01:50.000+0200},
  title = {Graph clustering},
  url = {http://www.sciencedirect.com/science/article/B8JDG-4PBG1S7-1/2/6537f3d1ffbf391086c60dbeba874b13},
  volume = 1,
  year = 2007
}

Endnote record