Results 11 to 20 of about 84,208 (297)
Learnable Graph Convolutional Attention Networks
CoRR, 2023 Existing Graph Neural Networks (GNNs) compute the message exchange between nodes by either aggregating uniformly (convolving) the features of all the neighboring nodes, or by applying a non-uniform score (attending) to the features.
Sanchez-Martin, Pablo +3 more
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Fusing multiplex heterogeneous networks using graph attention-aware fusion networks
Scientific Reports, 2023 Graph Neural Networks (GNN) emerged as a deep learning framework to generate node and graph embeddings for downstream machine learning tasks. Popular GNN-based architectures operate on networks of single node and edge type.
Ziynet Nesibe Kesimoglu, Serdar Bozdag
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2024 Twelfth International Symposium on Computing and Networking Workshops (CANDARW)Graphs are useful for representing various realworld objects. However, graph neural networks (GNNs) tend to suffer from over-smoothing, where the representations of nodes of different classes become similar as the number of layers increases, leading to ...
Gobara, Keita +3 more
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Hyperbolic Heterogeneous Graph Attention Networks
Companion Proceedings of the ACM Web Conference 2024Most previous heterogeneous graph embedding models represent elements in a heterogeneous graph as vector representations in a low-dimensional Euclidean space.
Han, Seunghoon +3 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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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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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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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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