Results 31 to 40 of about 8,080,419 (296)
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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Recipe Recommendation With Hierarchical Graph Attention Network
Frontiers in Big Data, 2022 Recipe recommendation systems play an important role in helping people find recipes that are of their interest and fit their eating habits. Unlike what has been developed for recommending recipes using content-based or collaborative filtering approaches,
Yijun Tian +3 more
doaj +1 more source
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 Speaker Verification [PDF]
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021 5 pages, 1 figure, 2 tables, accepted for presentation at ICASSP 2021 as a conference ...
Jee-weon Jung +3 more
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Phrase-level attention network for few-shot inverse relation classification in knowledge graph
, 2023 Relation classification aims to recognize semantic relation between two given entities mentioned in the given text. Existing models have performed well on the inverse relation classification with large-scale datasets, but their performance drops ...
Chunliu Dou +15 more
core +1 more source
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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Graph attention network-based fluid simulation model
AIP Advances, 2022 Traditional computational fluid dynamics (CFD) techniques deduce the dynamic variations in flow fields by using finite elements or finite differences to solve partial differential equations.
Qiang Liu +5 more
doaj +1 more source
Signed Graph Attention Networks [PDF]
, 2019 Accepted and to appear at ...
Junjie Huang +3 more
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Relational Graph Attention Networks
CoRR, 2019 We investigate Relational Graph Attention Networks, a class of models that extends non-relational graph attention mechanisms to incorporate relational information, opening up these methods to a wider variety of problems. A thorough evaluation of these models is performed, and comparisons are made against established benchmarks.
Dan Busbridge +3 more
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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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