Results 231 to 240 of about 106,924 (261)

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
exaly   +2 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   +1 more source

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
openaire   +1 more source

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
openaire   +1 more source

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
openaire   +1 more source

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
openaire   +1 more source

Weighted Feature Fusion of Convolutional Neural Network and Graph Attention Network for Hyperspectral Image Classification

IEEE Transactions on Image Processing, 2022
Yanni Dong, Quanwei Liu, Bo Du
exaly  

Hierarchical graph attention network for miRNA-disease association prediction

Molecular Therapy, 2022
Zhengwei Li, Tangbo Zhong, Zhu-Hong You   +2 more
exaly  

Multi-scale receptive fields: Graph attention neural network for hyperspectral image classification

Expert Systems With Applications, 2023
Yao Ding, Zhili Zhang, Xiaofeng Zhao
exaly  

Fairness-aware Graph Attention Networks

2022 56th Asilomar Conference on Signals, Systems, and Computers, 2022
Öykü Deniz Köse, Yanning Shen
openaire   +1 more source