Graph Processing Systems is a distributed data processing framework designed to manage and execute computational operations on graph-structured data by efficiently partitioning, storing, and processing large-scale graphs across multiple computing nodes using vertex-centric or edge-centric programming models.
584
Publications
36.8K
Citations
1.7K
Authors
449
Institutions
Publications per year
2017–2026
279
Parents
Publications
584
PowerGraph: distributed graph-parallel computation on natural graphs
Joseph E. Gonzalez, Yucheng Low, Haijie Gu et al. · 2012 · 1.5K citations
A scalable processing-in-memory accelerator for parallel graph processing
Junwhan Ahn, Sungpack Hong, Sungjoo Yoo et al. · 2015 · 761 citations
1–5 of 584
Authors
1.7K
- BH
18
h-index
- HJ
17
h-index
- DY
13
h-index
- XL
13
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- JX
12
h-index
1–5 of 1.7K
Institutions
449
Chinese University of Hong Kong
Hong Kong, Hong Kong
21 h-index
71 publications
4K citations
Tsinghua University
Beijing, China
19 h-index
73 publications
5.5K citations
National University of Singapore
Singapore, Singapore
18 h-index
46 publications
1.8K citations
Huazhong University of Science and Technology
Wuhan, China
18 h-index
140 publications
3.9K citations
Carnegie Mellon University
Pittsburgh, United States
17 h-index
49 publications
14.3K citations
Peking University
Beijing, China
17 h-index
48 publications
3.3K citations
Georgia Institute of Technology
Atlanta, United States
14 h-index
50 publications
3.1K citations
University of Waterloo
Waterloo, Canada
14 h-index
38 publications
3.7K citations
1–8 of 449
Venues
- Proceedings of the VLDB Endowment
PVLDB · 53 publications · 4.1K citations
32
h-index
- arXiv (Cornell University)
Repository · 26 publications · 672 citations
15
h-index
- IEEE Transactions on Parallel and Distributed Systems
TPDS · 17 publications · 592 citations
14
h-index
- IEEE Transactions on Knowledge and Data Engineering
TKDE · 12 publications · 557 citations
12
h-index
- Communications of the ACM
6 publications · 1.1K citations
6
h-index
1–5