CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2021-01-10
Cited: 0
Clicked: 6608
Citations: Bibtex RefMan EndNote GB/T7714
Jianke HU, Yin ZHANG. NGAT: attention in breadth and depth exploration for semi-supervised graph representation learning[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(3): 409-421.
@article{title="NGAT: attention in breadth and depth exploration for semi-supervised graph representation learning",
author="Jianke HU, Yin ZHANG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="3",
pages="409-421",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000657"
}
%0 Journal Article
%T NGAT: attention in breadth and depth exploration for semi-supervised graph representation learning
%A Jianke HU
%A Yin ZHANG
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 3
%P 409-421
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000657
TY - JOUR
T1 - NGAT: attention in breadth and depth exploration for semi-supervised graph representation learning
A1 - Jianke HU
A1 - Yin ZHANG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 3
SP - 409
EP - 421
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000657
Abstract: Recently, graph neural networks (GNNs) have achieved remarkable performance in representation learning on graph-structured data. However, as the number of network layers increases, GNNs based on the neighborhood aggregation strategy deteriorate due to the problem of oversmoothing, which is the major bottleneck for applying GNNs to real-world graphs. Many efforts have been made to improve the process of feature information aggregation from directly connected nodes, i.e., breadth exploration. However, these models perform the best only in the case of three or fewer layers, and the performance drops rapidly for deep layers. To alleviate oversmoothing, we propose a nested graph attention network (NGAT), which can work in a semi-supervised manner. In addition to breadth exploration, a
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