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Comparing Graph Sample and Aggregation (SAGE) and Graph Attention Networks in the Prediction of Drug-Gene Associations of Extended-Spectrum Beta-Lactamases in Periodontal Infections and Resistance. [PDF]

Cureus
Harris J, Yadalam PK, Anegundi RV, Arumuganainar D.   +3 more
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Structural attention network for graph

Applied Intelligence, 2021
We 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
exaly   +2 more sources

Graph neural networks with multiple kernel ensemble attention

Knowledge-Based Systems, 2021
Graph neural networks have recently attracted increasing research attention. Recent research has shown that applying the attention mechanism is helpful for graph learning. The performance of the attention function is critical for graph learning.
Min Xu, Haimin Zhang
exaly   +2 more sources

High-order graph attention network [PDF]

Information Sciences, 2023
GCN is a widely-used representation learning method for capturing hidden features in graph data. However, traditional GCNs suffer from the oversmoothing problem, hindering their ability to extract high-order information and obtain robust data representation.
Jiye Liang, Liang Bai, Xian Yang
exaly   +3 more sources

Heterogeneous Graph Gated Attention Network

2021 International Joint Conference on Neural Networks (IJCNN), 2021
Heterogeneous graph containing different types of nodes or links is one of graph types, which is most relevant to actual problems. However, the research for heterogeneous graph has not been studied adequately. In this paper, we propose a new model named Heterogeneous Graph Gated Attention Network (HGGAN) to process heterogeneous graph, including node ...
Shuai Ma, Jian-Wei Liu 0006, Xin Zuo, Weimin Li 0001   +3 more
openaire   +2 more sources

Heterogeneous Dynamic Graph Attention Network

2020 IEEE International Conference on Knowledge Graph (ICKG), 2020
Network embedding (graph embedding) has become the focus of studying graph structure in recent years. In addition to the research on homogeneous networks and heterogeneous networks, there are also some methods to attempt to solve the problem of dynamic network embedding.
Qiuyan Li, Yanlei Shang, Xiuquan Qiao, Wei Dai   +3 more
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Simple and deep graph attention networks

Knowledge-Based Systems
Xuemin Lin, Hanchen Wang
exaly   +2 more sources

Graph Attention Network for Word Embeddings

2021
The word embeddings approaches have attracted extensive attention and widely used in many natural language processing (NLP) tasks. Relatedness between words can be reflected in vector space by word embeddings. However, the current word embeddings approaches commonly do not explore the context-specific information of word deeply in the overall corpus ...
Yunfei Long, Huosheng Xu, Pengyuan Qi, Liguo Zhang, Jun Li   +4 more
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Graph Attention in Attention Network for Image Denoising

IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023
Bo Jiang 0017, Yao Lu 0008, Xiaosheng Chen, Xinhai Lu, Guangming Lu 0002   +4 more
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Multimodal heterogeneous graph attention network

Neural Computing and Applications, 2022
Xiangen Jia, Min Jiang 0005, Yihong Dong, Feng Zhu, Haocai Lin, Yu Xin, Huahui Chen 0001   +6 more
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