License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.ICCSW.2018.1
URN: urn:nbn:de:0030-drops-101829
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2019/10182/
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Liu, C. H. Bryan ; Chamberlain, Benjamin Paul

Speeding Up BigClam Implementation on SNAP

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OASIcs-ICCSW-2018-1.pdf (0.7 MB)

Abstract

We perform a detailed analysis of the C++ implementation of the Cluster Affiliation Model for Big Networks (BigClam) on the Stanford Network Analysis Project (SNAP). BigClam is a popular graph mining algorithm that is capable of finding overlapping communities in networks containing millions of nodes. Our analysis shows a key stage of the algorithm - determining if a node belongs to a community - dominates the runtime of the implementation, yet the computation is not parallelized. We show that by parallelizing computations across multiple threads using OpenMP we can speed up the algorithm by 5.3 times when solving large networks for communities, while preserving the integrity of the program and the result.

BibTeX - Entry

@InProceedings{liu_et_al:OASIcs:2019:10182,
  author =	{C. H. Bryan Liu and Benjamin Paul Chamberlain},
  title =	{{Speeding Up BigClam Implementation on SNAP}},
  booktitle =	{2018 Imperial College Computing Student Workshop (ICCSW 2018)},
  pages =	{1:1--1:13},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-097-2},
  ISSN =	{2190-6807},
  year =	{2019},
  volume =	{66},
  editor =	{Edoardo Pirovano and Eva Graversen},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2019/10182},
  URN =		{urn:nbn:de:0030-drops-101829},
  doi =		{10.4230/OASIcs.ICCSW.2018.1},
  annote =	{Keywords: BigClam, Community Detection, Parallelization, Networks}
}

Keywords: BigClam, Community Detection, Parallelization, Networks
Collection: 2018 Imperial College Computing Student Workshop (ICCSW 2018)
Issue Date: 2019
Date of publication: 25.01.2019

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