A study of graph partitioning schemes for parallel graph community detection

Jianping Zeng, Hongfeng Yu

Research output: Contribution to journalArticle

19 Scopus citations

Abstract

This paper presents a study of graph partitioning schemes for parallel graph community detection on distributed memory machines. We investigate the relationship between graph structure and parallel clustering effectiveness, and develop a heuristic partitioning algorithm suitable for modularity-based algorithms. We demonstrate the accuracy and scalability of our approach using several real-world large graph datasets compared with state-of-the-art parallel algorithms on the Cray XK7 supercomputer at Oak Ridge National Laboratory. Given the ubiquitous graph model, we expect this high-performance solution will help lead to new insights in numerous fields.

Original languageEnglish (US)
Pages (from-to)131-139
Number of pages9
JournalParallel Computing
Volume58
DOIs
StatePublished - Oct 1 2016

Keywords

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

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Networks and Communications
  • Computer Graphics and Computer-Aided Design
  • Artificial Intelligence

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