GUISE: Uniform sampling of graphlets for large graph analysis

Mansurul A. Bhuiyan, Mahmudur Rahman, Mahmuda Rahman, Mohammad Al Hasan

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

43 Citations (Scopus)

Abstract

Graphlet frequency distribution (GFD) has recently become popular for characterizing large networks. However, the computation of GFD for a network requires the exact count of embedded graphlets in that network, which is a computationally expensive task. As a result, it is practically infeasible to compute the GFD for even a moderately large network. In this paper, we propose GUISE, which uses a Markov Chain Monte Carlo (MCMC) sampling method for constructing the approximate GFD of a large network. Our experiments on networks with millions of nodes show that GUISE obtains the GFD within few minutes, whereas the exhaustive counting based approach takes several days.

Original languageEnglish
Title of host publicationProceedings - 12th IEEE International Conference on Data Mining, ICDM 2012
Pages91-100
Number of pages10
DOIs
Publication statusPublished - 1 Dec 2012
Event12th IEEE International Conference on Data Mining, ICDM 2012 - Brussels, Belgium
Duration: 10 Dec 201213 Dec 2012

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other12th IEEE International Conference on Data Mining, ICDM 2012
CountryBelgium
CityBrussels
Period10/12/1213/12/12

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Bhuiyan, M. A., Rahman, M., Rahman, M., & Al Hasan, M. (2012). GUISE: Uniform sampling of graphlets for large graph analysis. In Proceedings - 12th IEEE International Conference on Data Mining, ICDM 2012 (pp. 91-100). [6413912] (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDM.2012.87