Heterogeneous Graph Mining
Heterogeneous Graph Mining is a subfield of graph mining that focuses on extracting meaningful patterns and knowledge from graphs comprising multiple types of nodes and edges, distinguished by its emphasis on leveraging type-level heterogeneity to enhance representation learning, inference, and predictive modeling.
116
Publications
5.9K
Citations
509
Authors
179
Institutions
Publications per year
2017–2026
92
Parents
116
An Overview of Microsoft Academic Service (MAS) and Applications
Arnab Sinha, Z. Shen, Yang Song et al. · 2015 · 929 citations
Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification
Linmei Hu, Tianchi Yang, Chuan Shi et al. · 2019 · 339 citations · Full text
HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction
Xing Chen, Chenggang Yan, Xu Zhang et al. · Oncotarget · 2016 · 237 citations · Full text
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179

Beijing University of Posts and Telecommunications
Beijing, China
8 h-index
32 publications
2.1K citations
Indiana University Bloomington
Bloomington, United States
7 h-index
16 publications
320 citations

Dalian Maritime University
Dalian, China
5 h-index
7 publications
149 citations

Chinese Academy of Sciences
Beijing, China
5 h-index
8 publications
621 citations
Northeastern University
Boston, United States
5 h-index
5 publications
165 citations
Alibaba Group (United States)
San Mateo, United States
4 h-index
13 publications
470 citations
Shandong University
Jinan, China
4 h-index
11 publications
532 citations

Sun Yat-sen University
Guangzhou, China
4 h-index
10 publications
484 citations
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6 publications · 301 citations
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4 publications · 332 citations
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4 publications · 101 citations
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Conference · 3 publications · 121 citations
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2 publications · 42 citations
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