Results 31 to 40 of about 84,208 (297)
CoRR, 2017 We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.
Veličković, P +5 more
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Graph Attention Networks for Anti-Spoofing [PDF]
Interspeech 2021, 2021 Submitted to INTERSPEECH ...
Hemlata Tak +4 more
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Adaptive Depth Graph Attention Networks
CoRR, 2023 As one of the most popular GNN architectures, the graph attention networks (GAT) is considered the most advanced learning architecture for graph representation and has been widely used in various graph mining tasks with impressive results. However, since GAT was proposed, none of the existing studies have provided systematic insight into the ...
Jingbo Zhou +3 more
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Edge-Featured Graph Attention Network
CoRR, 2021 Lots of neural network architectures have been proposed to deal with learning tasks on graph-structured data. However, most of these models concentrate on only node features during the learning process. The edge features, which usually play a similarly important role as the nodes, are often ignored or simplified by these models.
Jun Chen, Haopeng Chen
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, 2022 Since their introduction, graph attention networks achieved outstanding results in graph representation learning tasks. However, these networks consider only pairwise relationships among nodes and then they are not able to fully exploit higher-order ...
Giusti, Lorenzo +5 more
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Dual-Attention Graph Convolutional Network [PDF]
, 2020 Graph convolutional networks (GCNs) have shown the powerful ability in text structure representation and effectively facilitate the task of text classification. However, challenges still exist in adapting GCN on learning discriminative features from texts due to the main issue of graph variants incurred by the textual complexity and diversity.
Xueya Zhang +4 more
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Signed Graph Attention Networks [PDF]
, 2019 Accepted and to appear at ...
Junjie Huang +3 more
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, 2023 Focusing Fine-Grained Action by Self-Attention-Enhanced Graph Neural Networks with Contrastive ...
Lei Lyu (13311267) +4 more
core +1 more source
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.
Liancheng He +4 more
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Graph Attention Networks with Positional Embeddings [PDF]
, 2021 Graph Neural Networks (GNNs) are deep learning methods which provide the current state of the art performance in node classification tasks. GNNs often assume homophily -- neighboring nodes having similar features and labels--, and therefore may not be at their full potential when dealing with non-homophilic graphs.
Liheng Ma +2 more
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