Dynamic Graph Representation
Dynamic Graph Representation is a formal model that captures the evolving structure of a graph over time by encoding temporal patterns of node and edge changes within a unified mathematical framework.
327
Publications
17K
Citations
1.2K
Authors
415
Institutions
Publications per year
2017–2026
201
Parents
327
Dynamic graph convolutional networks
Franco Manessi, Alessandro Rozza, Mario Manzo · Pattern Recognition · 2019 · 444 citations · Full text
DyNet: The Dynamic Neural Network Toolkit
Graham Neubig, Chris Dyer, Yoav Goldberg et al. · arXiv (Cornell University) · 2017 · 345 citations · Full text
1–5 of 327
1.2K
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Hong Kong University of Science and Technology, Hong Kong SAR China
4 publications · 138 citations
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4
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3
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1–5 of 1.2K
415

Centre National de la Recherche Scientifique
Paris, France
10 h-index
27 publications
626 citations

Chinese Academy of Sciences
Beijing, China
9 h-index
26 publications
1.9K citations

Beihang University
Beijing, China
9 h-index
28 publications
1.3K citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
8 h-index
13 publications
432 citations
University of Southern California
Los Angeles, United States
7 h-index
15 publications
1.7K citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
7 h-index
15 publications
833 citations
Georgia Institute of Technology
Atlanta, United States
7 h-index
16 publications
672 citations

Beijing University of Posts and Telecommunications
Beijing, China
6 h-index
15 publications
527 citations
1–8 of 415
Repository · 27 publications · 1.4K citations
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7 publications · 172 citations
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Conference · 6 publications · 664 citations
6
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7 publications · 517 citations
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PVLDB · 5 publications · 454 citations
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