Results 41 to 50 of about 1,049,471 (309)
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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How Attentive are Graph Attention Networks?
, 2021 Published in ICLR ...
Brody, Shaked, Alon, Uri, Yahav, Eran
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Heterogeneous Graph Attention Network [PDF]
The World Wide Web Conference, 2019 10 ...
Wang, Xiao +6 more
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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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Personalized Pagerank Graph Attention Networks
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022 Published as a conference paper at ICASSP ...
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On the strengths of connectivity and robustness in general random intersection graphs [PDF]
, 2014 Random intersection graphs have received much attention for nearly two decades, and currently have a wide range of applications ranging from key predistribution in wireless sensor networks to modeling social networks.
Gligor, Virgil, Yağan, Osman, Zhao, Jun
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Learning Structure-From-Motion with Graph Attention Networks [PDF]
Computer Vision and Pattern Recognition, 2023 In this paper we tackle the problem of learning Structure-from-Motion (SfM) through the use of graph attention networks. SfM is a classic computer vision problem that is solved though iterative minimization of reprojection errors, referred to as Bundle ...
Lucas Brynte +3 more
semanticscholar +1 more source
Discriminating different classes of biological networks by analyzing the graphs spectra distribution [PDF]
, 2012 The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology.
Andre ́ Fujita +5 more
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IEEE Transactions on Knowledge and Data Engineering, 2023 Knowledge graph entity typing (KGET) aims to infer missing entity typing instances in KGs, which is a significant subtask of KG completion. Despite of its progress, however, we observe that it still faces two non-trivial challenges: (i) most existing ...
Yu Zhao +5 more
semanticscholar +1 more source
Composition of Biochemical Networks using Domain Knowledge [PDF]
, 2010 Graph composition has applications in a variety of practical applications. In drug development, for instance, in order to understand possible drug interactions, one has to merge known networks and examine topological variants arising from such ...
Ela Hunt +2 more
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