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 language | English (US) |
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Pages (from-to) | 131-139 |
Number of pages | 9 |
Journal | Parallel Computing |
Volume | 58 |
DOIs | |
State | Published - 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