Parallel Edge Contraction for Large Nonplanar Graph Clustering
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Ruo AndoAuthors Info & Claims
Ruo AndoAuthors Info & ClaimsPages 61 - 64
Abstract
With the flowering of graph mining and computation technology, the field of graph clustering has become popular. Particularly, to cluster increasingly massive data represented as graph become common today. There have been many research efforts on graph clustering algorithms as well as reported applications. However, graph clustering technology is highly application specific and computationally expensive especially when applied to huge data. In this paper, we propose an efficient graph contraction algorithm for speeding up graph clustering. We target at parallelizing edge contraction for reducing computing time for graph mining. We adopt massive multithreading for edge contraction on huge non planar graph. In experiment, our algorithm achieves good parallel efficiency for computation time of both contracting edges and clustering graph as the number of threads increases. Our edge contraction algorithm achieves the speedup ranging from 3 to 6 in contracting 10000 to 30000 edges. Finally, we observe that with edge contraction, parallel clustering achieves speed up is 8 to 80 times faster compared with the result of no edge contraction.
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- Parallel Edge Contraction for Large Nonplanar Graph Clustering
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Published In
October 2018
217 pages
ISBN:9781450365192
DOI:10.1145/3289430
Copyright © 2018 ACM.
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- Deakin University
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Association for Computing Machinery
New York, NY, United States
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Published: 24 October 2018
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BDIOT 2018
BDIOT 2018: 2018 2nd International Conference on Big Data and Internet of Things
October 24 - 26, 2018
Beijing, China
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Overall Acceptance Rate 75 of 136 submissions, 55%
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