A study of graph partitioning schemes for parallel graph community detection

Jianping Zeng, Hongfeng Yu

Research output: Contribution to journalArticle

14 Citations (Scopus)

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

Fingerprint

Community Detection
Graph Partitioning
Supercomputers
Heuristic algorithms
Parallel algorithms
Scalability
Data storage equipment
Graph in graph theory
Supercomputer
Distributed Memory
Graph Model
Ridge
Modularity
Parallel Algorithms
Partitioning
High Performance
Clustering
Heuristics
Demonstrate

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

Cite this

A study of graph partitioning schemes for parallel graph community detection. / Zeng, Jianping; Yu, Hongfeng.

In: Parallel Computing, Vol. 58, 01.10.2016, p. 131-139.

Research output: Contribution to journalArticle

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