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RNA language model and graph attention network for RNA and small molecule binding sites prediction. [PDF]
BioinformaticsSun S, Yang J, Gao L, Li P, Liu Y.
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Dynamic Allocation of C-V2X Communication Resources Based on Graph Attention Network and Deep Reinforcement Learning. [PDF]
Sensors (Basel)Li Z, Li G, Wu Z, Zhang W, Bazzi A.
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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
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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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Heterogeneous Graph Gated Attention Network
2021 International Joint Conference on Neural Networks (IJCNN), 2021Heterogeneous 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 +3 more
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Heterogeneous Dynamic Graph Attention Network
2020 IEEE International Conference on Knowledge Graph (ICKG), 2020Network 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 +3 more
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Simple and deep graph attention networks
Knowledge-Based SystemsXuemin Lin, Hanchen Wang
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Graph Attention Network for Word Embeddings
2021The 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 +4 more
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Graph Attention in Attention Network for Image Denoising
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023Bo Jiang 0017 +4 more
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