Graph Processing
Graph Processing is a computational paradigm that specializes in the efficient manipulation and analysis of graph-structured data through vertex-edge representations, distinguishing itself from other GPU computing methods by its focus on irregular, sparse, and highly connected data traversals.
2K
Publications
132.4K
Citations
5.3K
Authors
1.2K
Institutions
Publications per year
2017–2026
1.1K
2K
Deep Learning On Graphs (Graphsip Summer School)
Michaël Defferrard · Figshare · 2016 · 5.1K citations
Andrei Broder, Ravi Kumar, Farzin Maghoul et al. · Computer Networks · 2000 · 2.8K citations
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
2018 · 2.8K citations · Full text
Graph Signal Processing: Overview, Challenges, and Applications
Antonio Ortega, Pascal Frossard, Jelena Kovačević et al. · Proceedings of the IEEE · 2018 · 1.8K citations · Full text
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5.3K
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1.2K
Carnegie Mellon University
Pittsburgh, United States
34 h-index
123 publications
19K citations
Tsinghua University
Beijing, China
33 h-index
173 publications
11.5K citations

Peking University
Beijing, China
31 h-index
130 publications
7.5K citations
University of Southern California
Los Angeles, United States
29 h-index
91 publications
7.5K citations
Georgia Institute of Technology
Atlanta, United States
28 h-index
121 publications
7.8K citations
National University of Singapore
Singapore, Singapore
27 h-index
102 publications
5.5K citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
26 h-index
82 publications
5.4K citations

Huazhong University of Science and Technology
Wuhan, China
25 h-index
242 publications
7.5K citations
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Repository · 133 publications · 9.5K citations
43
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PVLDB · 58 publications · 4.3K citations
32
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28 publications · 2.5K citations
25
h-index
TKDE · 36 publications · 1.6K citations
22
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TPDS · 29 publications · 1.3K citations
22
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1–5