Neighborhood Aggregation
Neighborhood Aggregation is a computational mechanism in graph-based machine learning that, as a form of message-passing operation, selectively pools feature information from a node's adjacent neighbors according to a specified aggregation function to update the node's representation in a topology-aware manner.
123
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12.3K
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454
Authors
165
Institutions
Publications per year
2017–2026
105
123
Self-supervised Graph Learning for Recommendation
2021 · 1.3K citations · Full text
Representation Learning on Graphs with Jumping Knowledge Networks
Keyulu Xu, Chengtao Li, Yonglong Tian et al. · arXiv (Cornell University) · 2018 · 733 citations · Full text
Towards Deeper Graph Neural Networks
Meng Liu, Hongyang Gao, Shuiwang Ji · 2020 · 501 citations · Full text
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
Xiangnan He, Kuan Deng, Xiang Wang et al. · arXiv (Cornell University) · 2020 · 413 citations · Full text
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165
National University of Singapore
Singapore, Singapore
8 h-index
14 publications
3.5K citations
Massachusetts Institute of Technology
Cambridge, United States
5 h-index
13 publications
2.4K citations
University of Science and Technology of China
Hefei, China
5 h-index
17 publications
3.3K citations
Tsinghua University
Beijing, China
4 h-index
7 publications
334 citations
Peng Cheng Laboratory
Shenzhen, China
4 h-index
6 publications
147 citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
4 h-index
5 publications
215 citations
Singapore Management University
Singapore, Singapore
4 h-index
6 publications
130 citations

Peking University
Beijing, China
4 h-index
5 publications
227 citations
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Repository · 26 publications · 2.2K citations
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TKDE · 6 publications · 151 citations
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Conference · 5 publications · 317 citations
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3 publications · 124 citations
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3 publications · 78 citations
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