Parallel modularity-based community detection on large-scale graphs

Jianping Zeng, Hongfeng Yu

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

9 Scopus citations

Abstract

We present a parallel hierarchical graph clustering algorithm that uses modularity as clustering criteria to effectively extract community structures in large graphs of different types. In order to process a large complex graph (whose vertex number and edge number are around 1 billion), we design our algorithm based on the Louvain method by investigating graph partitioning and distribution schemes on distributed memory architectures and conducting clustering in a divide-and-conquer manner. We study the relationship between graph structure property and clustering quality, carefully deal with ghost vertices between graph partitions, and propose a heuristic partition method suitable for the Louvain method. Compared to the existing solutions, our method can achieve nearly well-balanced workload among processors and higher accuracy of graph clustering on real-world large graph datasets.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE International Conference on Cluster Computing, CLUSTER 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-10
Number of pages10
ISBN (Electronic)9781467365987
DOIs
StatePublished - Oct 26 2015
EventIEEE International Conference on Cluster Computing, CLUSTER 2015 - Chicago, United States
Duration: Sep 8 2015Sep 11 2015

Publication series

NameProceedings - IEEE International Conference on Cluster Computing, ICCC
Volume2015-October
ISSN (Print)1552-5244

Other

OtherIEEE International Conference on Cluster Computing, CLUSTER 2015
CountryUnited States
CityChicago
Period9/8/159/11/15

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Keywords

  • Community detection
  • Graph clustering
  • Large graph
  • Parallel and distributed processing

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Signal Processing

Cite this

Zeng, J., & Yu, H. (2015). Parallel modularity-based community detection on large-scale graphs. In Proceedings - 2015 IEEE International Conference on Cluster Computing, CLUSTER 2015 (pp. 1-10). [7307558] (Proceedings - IEEE International Conference on Cluster Computing, ICCC; Vol. 2015-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CLUSTER.2015.11