Single-Source Shortest Path Tree for Big Dynamic Graphs
Computing single-source shortest paths (SSSP) is one of the fundamental problems in graph theory. There are many applications of SSSP including finding routes in GPS systems and finding high centrality vertices for effective vaccination. In this paper, we focus on calculating SSSP on big dynamic graphs, which change with time. We propose a novel distributed computing approach, SSSPIncJoint, to update SSSP on big dynamic graphs using GraphX. Our approach considerably speeds up the recomputation of the SSSP tree by reducing the number of map-reduce operations required for implementing SSSP in the gather-apply- scatter programming model used by GraphX.
S. Riazi et al., "Single-Source Shortest Path Tree for Big Dynamic Graphs," Proceedings of the 2018 IEEE International Conference on Big Data (2018, Seattle, WA), pp. 4054-4062, Institute of Electrical and Electronics Engineers (IEEE), Dec 2019.
The definitive version is available at https://doi.org/10.1109/BigData.2018.8622042
2018 IEEE International Conference on Big Data, Big Data 2018 (2018: Dec. 10-13, Seattle, WA)
Intelligent Systems Center
Second Research Center/Lab
Center for Research in Energy and Environment (CREE)
Third Research Center/Lab
Center for High Performance Computing Research
Keywords and Phrases
Apache Spark; Big Dynamic Graphs; Map- Reduce; Single-Source Shortest Path (SSSP)
International Standard Book Number (ISBN)
Article - Conference proceedings
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01 Dec 2019