Results 11 to 20 of about 8,080,419 (296)
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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Gated Graph Attention Network for Cancer Prediction
Sensors, 2021 With its increasing incidence, cancer has become one of the main causes of worldwide mortality. In this work, we mainly propose a novel attention-based neural network model named Gated Graph ATtention network (GGAT) for cancer prediction, where a gating ...
Linling Qiu +3 more
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MIGAN: Mutual-Interaction Graph Attention Network for Collaborative Filtering
Entropy, 2022 Many web platforms now include recommender systems. Network representation learning has been a successful approach for building these efficient recommender systems. However, learning the mutual influence of nodes in the network is challenging. Indeed, it
Ahlem Drif, Hocine Cherifi
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MEGAN: Multi-explanation Graph Attention Network [PDF]
, 2023 We propose a multi-explanation graph attention network (MEGAN). Unlike existing graph explainability methods, our network can produce node and edge attributional explanations along multiple channels, the number of which is independent of task specifications.
Jonas Teufel +3 more
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Multi-Scale Dense Graph Attention Network for Hyperspectral Classification
Canadian Journal of Remote SensingIn recent years, numerous deep learning-based methods have gained increasing attention in hyperspectral classification, particularly the Graph Neural Network, which exhibits superior capabilities in structural description.
Chen Wang +8 more
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Hyperbolic Graph Attention Network [PDF]
IEEE Transactions on Big Data, 2021 Graph neural network (GNN) has shown superior performance in dealing with graphs, which has attracted considerable research attention recently. However, most of the existing GNN models are primarily designed for graphs in Euclidean spaces. Recent research has proven that the graph data exhibits non-Euclidean latent anatomy.
Yiding Zhang +4 more
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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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Graph Ordering Attention Networks
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 Graph Neural Networks (GNNs) have been successfully used in many problems involving graph-structured data, achieving state-of-the-art performance. GNNs typically employ a message-passing scheme, in which every node aggregates information from its neighbors using a permutation-invariant aggregation function.
Michail Chatzianastasis +3 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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