A study of graph partitioning schemes for parallel graph community detection |
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| Affiliation: | 1. School of Economics and Management, Fuzhou University, Fuzhou, 350116, China;2. College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China;3. Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou, 350116, China;4. Key Laboratory of Spatial Data Mining & Information Sharing, Ministry of Education, Fuzhou 350002, China;5. Department of Business and Computer Science, Southwestern Oklahoma State University, OK 74074, USA;1. KAIST Institute for IT Convergence, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea;2. Department of Knowledge Service Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea |
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| 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. |
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