Distributed Graph Processing
Distributed Graph Processing is a computational framework within distributed memory systems that enables the parallel execution of graph algorithms across multiple interconnected processors by partitioning and coordinating graph data and operations to achieve scalable and efficient processing of large-scale graph structures.
459
Publications
32K
Citations
1.3K
Authors
415
Institutions
Publications per year
2017–2026
172
459
Michael Isard, Mihai Budiu, Yuan Yu et al. · 2007 · 2.5K citations
PowerGraph: distributed graph-parallel computation on natural graphs
Joseph E. Gonzalez, Yucheng Low, Haijie Gu et al. · 2012 · 1.5K citations
Locality in Distributed Graph Algorithms
Nathan Linial · SIAM Journal on Computing · 1992 · 885 citations
Julian Shun, Guy E. Blelloch · 2013 · 808 citations
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415
Chinese University of Hong Kong
Hong Kong, Hong Kong
19 h-index
62 publications
3.5K citations
Carnegie Mellon University
Pittsburgh, United States
15 h-index
25 publications
11.9K citations
Google (United States)
Mountain View, United States
14 h-index
24 publications
2.4K citations

University of Waterloo
Waterloo, Canada
13 h-index
25 publications
1.7K citations

Shanghai Jiao Tong University
Shanghai, China
12 h-index
53 publications
2.8K citations
The University of Texas at Austin
Austin, United States
11 h-index
44 publications
1.5K citations

Peking University
Beijing, China
11 h-index
23 publications
2.1K citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
10 h-index
11 publications
970 citations
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PVLDB · 43 publications · 3.8K citations
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Repository · 23 publications · 661 citations
15
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TPDS · 17 publications · 570 citations
15
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TKDE · 10 publications · 330 citations
10
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FGCS · 5 publications · 107 citations
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