Parallel Algorithm for Incremental Betweenness Centrality on Large Graphs

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 perfo...

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Bibliographic Details
Published in:IEEE transactions on parallel and distributed systems Vol. 29; no. 3; pp. 659 - 672
Main Authors: Jamour, Fuad, Skiadopoulos, Spiros, Kalnis, Panos
Format: Journal Article
Language:English
Published: New York IEEE 01.03.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithm design and analysis
Algorithms
Betweenness centrality
dynamic graph algorithms
Graphs
Heuristic algorithms
Measurement
Memory management
Network analysis
Parallel algorithms
parallel graph algorithms
Random access memory
Social network services
State of the art
ISSN:1045-9219, 1558-2183
Online Access:Get full text
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Summary: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.
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ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2017.2763951