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Structural attention network for graph
Applied Intelligence, 2021We present a structural attention network (SAN) for graph modeling, which is a novel approach to learn node representations based on graph attention networks (GATs), with the introduction of two improvements specially designed for graph-structured data. The transition matrix was used to differentiate the structures between the nodes.
Anzhong Zhou, Yifen Li
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Meta-structure-based graph attention networks
Neural NetworksDue to the ubiquity of graph-structured data, Graph Neural Network (GNN) have been widely used in different tasks and domains and good results have been achieved in tasks such as node classification and link prediction. However, there are still many challenges in representation learning of heterogeneous networks.
Jin Li +3 more
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Adaptive Attention Graph Capsule Network
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022Xiangping Zheng +4 more
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Attention-based graph neural networks: a survey
Artificial Intelligence Review, 2023, Zhixiao Wang, Fanrong Meng
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Learning Knowledge Graph Embedding With Heterogeneous Relation Attention Networks
IEEE Transactions on Neural Networks and Learning Systems, 2022Zhifei Li, Hai Liu, Zhaoli Zhang
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Multimodal heterogeneous graph attention network
Neural Computing and Applications, 2022Xiangen Jia +6 more
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Attention based spatiotemporal graph attention networks for traffic flow forecasting
Information Sciences, 2022Yi Wang +3 more
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Sparse Graph Attention Networks
IEEE Transactions on Knowledge and Data Engineering, 2021Yang Ye, Shihao Ji
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