Graph-based Pattern Recognition
Graph-Based Pattern Recognition is a pattern recognition approach that uses graph structures as the fundamental representation for modeling objects and their relational attributes, distinguishing itself by leveraging topological and structural relationships among entities to facilitate recognition tasks.
405
Publications
96K
Citations
1K
Authors
434
Institutions
Publications per year
2017–2026
107
Parents
405
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio et al. · Proceedings of the IEEE · 1998 · 58.2K citations · Full text
The Graph Neural Network Model
Franco Scarselli, M. Gori, Ah Chung Tsoi et al. · IEEE Transactions on Neural Networks · 2008 · 9.3K citations · Full text
1–5 of 405
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434

Centre National de la Recherche Scientifique
Paris, France
11 h-index
17 publications
780 citations

Sapienza University of Rome
Rome, Italy
8 h-index
18 publications
460 citations
University of Bern
Bern, Switzerland
8 h-index
15 publications
1K citations
Carnegie Mellon University
Pittsburgh, United States
7 h-index
13 publications
751 citations
Nanyang Technological University
Singapore, Singapore
7 h-index
14 publications
468 citations
Universidade de São Paulo
São Paulo, Brazil
7 h-index
15 publications
458 citations

Chinese Academy of Sciences
Beijing, China
7 h-index
23 publications
1.2K citations

Universitat Rovira i Virgili
Tarragona, Spain
6 h-index
7 publications
209 citations
1–8 of 434
23 publications · 1.2K citations
18
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15 publications · 920 citations
14
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12 publications · 2.1K citations
11
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TPAMI · 10 publications · 3.8K citations
10
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8 publications · 1.7K citations
8
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