Parallel Algorithm for Incremental Betweenness Centrality on Large Graphs

Handle URI:
http://hdl.handle.net/10754/625935
Title:
Parallel Algorithm for Incremental Betweenness Centrality on Large Graphs
Authors:
Jamour, Fuad Tarek; Skiadopoulos, Spiros; Kalnis, Panos ( 0000-0002-5060-1360 )
Abstract:
Betweenness centrality quantifies the importance of nodes in a graph in many applications, including network analysis, community detection and identification of influential users. Typically, graphs in such applications evolve over time. Thus, the computation of betweenness centrality should be performed incrementally. This is challenging because updating even a single edge may trigger the computation of all-pairs shortest paths in the entire graph. Existing approaches cannot scale to large graphs: they either require excessive memory (i.e., quadratic to the size of the input graph) or perform unnecessary computations rendering them prohibitively slow. We propose iCentral; a novel incremental algorithm for computing betweenness centrality in evolving graphs. We decompose the graph into biconnected components and prove that processing can be localized within the affected components. iCentral is the first algorithm to support incremental betweeness centrality computation within a graph component. This is done efficiently, in linear space; consequently, iCentral scales to large graphs. We demonstrate with real datasets that the serial implementation of iCentral is up to 3.7 times faster than existing serial methods. Our parallel implementation that scales to large graphs, is an order of magnitude faster than the state-of-the-art parallel algorithm, while using an order of magnitude less computational resources.
KAUST Department:
Computer Science, King Abdullah University of Science and Technology, 127355 Thuwal, Makkah Saudi Arabia; Computer Science, King Abdullah University of Science and Technology, Thuwal, Jeddah Saudi Arabia 23955
Citation:
Jamour F, Skiadopoulos S, Kalnis P (2017) Parallel Algorithm for Incremental Betweenness Centrality on Large Graphs. IEEE Transactions on Parallel and Distributed Systems: 1–1. Available: http://dx.doi.org/10.1109/tpds.2017.2763951.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Parallel and Distributed Systems
Issue Date:
17-Oct-2017
DOI:
10.1109/tpds.2017.2763951
Type:
Article
ISSN:
1045-9219
Additional Links:
http://ieeexplore.ieee.org/document/8070346/
Appears in Collections:
Articles

Full metadata record

DC FieldValue Language
dc.contributor.authorJamour, Fuad Tareken
dc.contributor.authorSkiadopoulos, Spirosen
dc.contributor.authorKalnis, Panosen
dc.date.accessioned2017-10-24T10:45:42Z-
dc.date.available2017-10-24T10:45:42Z-
dc.date.issued2017-10-17en
dc.identifier.citationJamour F, Skiadopoulos S, Kalnis P (2017) Parallel Algorithm for Incremental Betweenness Centrality on Large Graphs. IEEE Transactions on Parallel and Distributed Systems: 1–1. Available: http://dx.doi.org/10.1109/tpds.2017.2763951.en
dc.identifier.issn1045-9219en
dc.identifier.doi10.1109/tpds.2017.2763951en
dc.identifier.urihttp://hdl.handle.net/10754/625935-
dc.description.abstractBetweenness centrality quantifies the importance of nodes in a graph in many applications, including network analysis, community detection and identification of influential users. Typically, graphs in such applications evolve over time. Thus, the computation of betweenness centrality should be performed incrementally. This is challenging because updating even a single edge may trigger the computation of all-pairs shortest paths in the entire graph. Existing approaches cannot scale to large graphs: they either require excessive memory (i.e., quadratic to the size of the input graph) or perform unnecessary computations rendering them prohibitively slow. We propose iCentral; a novel incremental algorithm for computing betweenness centrality in evolving graphs. We decompose the graph into biconnected components and prove that processing can be localized within the affected components. iCentral is the first algorithm to support incremental betweeness centrality computation within a graph component. This is done efficiently, in linear space; consequently, iCentral scales to large graphs. We demonstrate with real datasets that the serial implementation of iCentral is up to 3.7 times faster than existing serial methods. Our parallel implementation that scales to large graphs, is an order of magnitude faster than the state-of-the-art parallel algorithm, while using an order of magnitude less computational resources.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/document/8070346/en
dc.rights(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en
dc.subjectBetweenness centralityen
dc.subjectdynamic graph algorithmsen
dc.subjectparallel graph algorithmsen
dc.titleParallel Algorithm for Incremental Betweenness Centrality on Large Graphsen
dc.typeArticleen
dc.contributor.departmentComputer Science, King Abdullah University of Science and Technology, 127355 Thuwal, Makkah Saudi Arabiaen
dc.contributor.departmentComputer Science, King Abdullah University of Science and Technology, Thuwal, Jeddah Saudi Arabia 23955en
dc.identifier.journalIEEE Transactions on Parallel and Distributed Systemsen
dc.eprint.versionPost-printen
dc.contributor.institutionDepartment of Computer Science and Technology, University of Peloponnese, Tripolis, Arcadia Greece 22100en
kaust.authorJamour, Fuad Tareken
kaust.authorKalnis, Panosen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.