Results 21 to 30 of about 84,208 (297)
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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Domain-Adaptive Graph Attention-Supervised Network for Cross-Network Edge Classification
, 2023 Graph neural networks (GNNs) have shown great ability in modeling graphs; however, their performance would significantly degrade when there are noisy edges connecting nodes from different classes.
Shao, M +4 more
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Directional Graph Attention Networks
, 2023 In recent years, graph neural networks (GNNs) have become a promising method for analyzing data structured in graph format. By considering connections between entities in a graph, GNNs are able to extract valuable insights. One notable variation of GNN is the graph attention network (GAT), which employs the attention mechanism and has demonstrated ...
Sitao Luan, Jiaqi Zhu
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SGAT: Simplicial Graph Attention Network
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022 Heterogeneous graphs have multiple node and edge types and are semantically richer than homogeneous graphs. To learn such complex semantics, many graph neural network approaches for heterogeneous graphs use metapaths to capture multi-hop interactions between nodes.
See Hian Lee, Feng Ji, Wee Peng Tay
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GAEN: Graph Attention Evolving Networks [PDF]
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021 Real-world networked systems often show dynamic properties with continuously evolving network nodes and topology over time. When learning from dynamic networks, it is beneficial to correlate all temporal networks to fully capture the similarity/relevance between nodes. Recent work for dynamic network representation learning typically trains each single
Min Shi 0001 +5 more
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Graph Tree Networks: a graph representation learning framework
, 2023 Fang, XiaoGraph Neural Networks (GNNs) have been successfully applied in many areas to solve real-world problems. Among various architectures of GNNs, the class of spatial-based convolutional GNNs (Conv-GNNs) has gained particular attention due to its ...
Wu, Nan
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How Attentive are Graph Attention Networks?
CoRR, 2021 Published in ICLR ...
Shaked Brody, Uri Alon 0002, Eran Yahav
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STAGCN: Spatial–Temporal Attention Graph Convolution Network for Traffic Forecasting
Mathematics, 2022 Traffic forecasting plays an important role in intelligent transportation systems. However, the prediction task is highly challenging due to the mixture of global and local spatiotemporal dependencies involved in traffic data.
Yafeng Gu, Li Deng
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LEHAN: Link-Feature Enhanced Heterogeneous Graph Attention Network
IEEE Access, 2022 Graph Neural Networks (GNNs) have been studied extensively and have performed well in solving complex machine learning tasks in recent years. Many GNN-based approaches focused on representing homogeneous graphs with only a single type of nodes and links.
Jongmin Park, Sungsu Lim
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, 2022 Traffic forecasting is an integral part of intelligent transportation systems (ITS). Achieving a high prediction accuracy is a challenging task due to a high level of dynamics and complex spatial-temporal dependency of road networks. For this task, we
Yang, Song +5 more
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