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
Main Authors: Edmonds, N, Hoefler, T, Lumsdaine, A
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2010
Subjects:
Algorithm design and analysis
Data structures
Laboratories
Memory management
Parallel algorithms
Social network services
ISBN:9781424485185, 1424485185
ISSN:1094-7256
Online Access:Get full text
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Abstract 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.
AbstractList 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.
Author Lumsdaine, A
Hoefler, T
Edmonds, N
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  email: htor@Illinois.edu
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  givenname: A
  surname: Lumsdaine
  fullname: Lumsdaine, A
  email: lums@osl.iu.edu
  organization: Open Syst. Lab., Indiana Univ., Bloomington, IN, USA
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Snippet Betweenness centrality is a measure based on shortest paths that attempts to quantify the relative importance of nodes in a network. As computation of...
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SubjectTerms Algorithm design and analysis
Data structures
Laboratories
Memory management
Parallel algorithms
Social network services
Title A space-efficient parallel algorithm for computing betweenness centrality in distributed memory
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