Graph-based Fusion
Graph-Based Fusion is a data integration framework that uses graph structures to model relationships among heterogeneous data sources, distinguishing itself by leveraging node-edge representations to enable joint inference and information propagation across modalities in multi-omics, multisensor, and object detection contexts.
171
Publications
11.3K
Citations
743
Authors
270
Institutions
Publications per year
2017–2026
144
171
HGNN<sup>+</sup>: General Hypergraph Neural Networks
Yue Gao, Yifan Feng, Shuyi Ji et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2022 · 462 citations
Multi-graph fusion for multi-view spectral clustering
Zhao Kang, Guoxin Shi, Shudong Huang et al. · Knowledge-Based Systems · 2019 · 287 citations
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270

Harbin Institute of Technology
Harbin, China
10 h-index
24 publications
1.1K citations

University of Electronic Science and Technology of China
Chengdu, China
9 h-index
27 publications
2.7K citations

Chinese Academy of Sciences
Beijing, China
8 h-index
29 publications
1.4K citations

Huazhong University of Science and Technology
Wuhan, China
7 h-index
16 publications
804 citations
University of Chinese Academy of Sciences
Beijing, China
6 h-index
13 publications
556 citations

National University of Defense Technology
Changsha, China
6 h-index
21 publications
1.2K citations
University at Buffalo, State University of New York
Buffalo, United States
6 h-index
15 publications
338 citations
GIPSA-Lab
Grenoble, France
5 h-index
5 publications
147 citations
1–8 of 270
Repository · 7 publications · 329 citations
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9 publications · 363 citations
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6 publications · 609 citations
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6 publications · 581 citations
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TKDE · 5 publications · 118 citations
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