A space-efficient parallel algorithm for computing betweenness centrality in distributed memory
Betweenness centrality is a measure based on shortest paths that attempts to quantify the relative importance of nodes in a network. As computation of betweenness centrality becomes increasingly important in areas such as social network analysis, networks of interest are becoming too large to fit in...
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| Published in: | 2010 International Conference on High Performance Computing pp. 1 - 10 |
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| Main Authors: | , , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
IEEE
01.12.2010
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| Subjects: | |
| ISBN: | 9781424485185, 1424485185 |
| ISSN: | 1094-7256 |
| Online Access: | Get full text |
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| Summary: | Betweenness centrality is a measure based on shortest paths that attempts to quantify the relative importance of nodes in a network. As computation of betweenness centrality becomes increasingly important in areas such as social network analysis, networks of interest are becoming too large to fit in the memory of a single processing unit, making parallel execution a necessity. Parallelization over the vertex set of the standard algorithm, with a final reduction of the centrality for each vertex, is straightforward but requires Ω(|V| 2 ) storage. In this paper we present a new parallelizable algorithm with low spatial complexity that is based on the best known sequential algorithm. Our algorithm requires O(|V| + |E|) storage and enables efficient parallel execution. Our algorithm is especially well suited to distributed memory processing because it can be implemented using coarse-grained parallelism. The presented time bounds for parallel execution of our algorithm on CRCW PRAM and on distributed memory systems both show good asymptotic performance. Experimental results with a distributed memory computer show the practical applicability of our algorithm. |
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| ISBN: | 9781424485185 1424485185 |
| ISSN: | 1094-7256 |
| DOI: | 10.1109/HIPC.2010.5713180 |

