Results 21 to 30 of about 6,341,676 (193)
Advances in Knowledge Graph Embedding Based on Graph Neural Networks [PDF]
Jisuanji kexue yu tansuo, 2023 As graph neural networks continue to develop, knowledge graph embedding methods based on graph neural networks are receiving increasing attention from researchers.
YAN Zhaoyao, DING Cangfeng, MA Lerong, CAO Lu, YOU Hao
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Sparse Graph Attention Networks [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2023 Graph Neural Networks (GNNs) have proved to be an effective representation learning framework for graph-structured data, and have achieved state-of-the-art performance on many practical predictive tasks, such as node classification, link prediction and graph classification.
Yang Ye, Shihao Ji 0001
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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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Graph Attention Networks for Anti-Spoofing [PDF]
Interspeech 2021, 2021 Submitted to INTERSPEECH ...
Hemlata Tak +4 more
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Heterogeneous Graph Attention Network [PDF]
The World Wide Web Conference, 2019 10 ...
Xiao Wang 0017 +6 more
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A Regularized Attention Mechanism for Graph Attention Networks [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 Machine learning models that can exploit the inherent structure in data have gained prominence. In particular, there is a surge in deep learning solutions for graph-structured data, due to its wide-spread applicability in several fields. Graph attention networks (GAT), a recent addition to the broad class of feature learning models in graphs, utilizes ...
Uday Shankar Shanthamallu +2 more
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MBHAN: Motif-Based Heterogeneous Graph Attention Network
Applied Sciences, 2022 Graph neural networks are graph-based deep learning technologies that have attracted significant attention from researchers because of their powerful performance. Heterogeneous graph-based graph neural networks focus on the heterogeneity of the nodes and
Qian Hu +3 more
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Knowledge Graph Embedding via Graph Attenuated Attention Networks
IEEE Access, 2020 Knowledge graphs contain a wealth of real-world knowledge that can provide strong support for artificial intelligence applications. Much progress has been made in knowledge graph completion, state-of-the-art models are based on graph convolutional neural
Rui Wang +4 more
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Heterophily-aware graph attention network
Pattern Recognition, 2023 Graph Neural Networks (GNNs) have shown remarkable success in graph representation learning. Unfortunately, current weight assignment schemes in standard GNNs, such as the calculation based on node degrees or pair-wise representations, can hardly be effective in processing the networks with heterophily, in which the connected nodes usually possess ...
Junfu Wang +3 more
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Enhancing Facial Reconstruction Using Graph Attention Networks
IEEE Access, 2023 Traditionally, research on three-dimensional (3D) facial reconstruction has focused heavily on methods that use 3D Morphable Models (3DMMs) based on principal component analysis (PCA).
Hyeong Geun Lee +3 more
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