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A heterogeneous graph neural network with attribute enhancement and structure-aware attention
S Fan, G Liu, J Li - IEEE Transactions on Computational Social …, 2023 - ieeexplore.ieee.org
Heterogeneous information network (HIN) has been applied in a wide variety of graph
analysis tasks. At present, it is a trend of heterogeneous graph neural networks (HGNNs) to …
analysis tasks. At present, it is a trend of heterogeneous graph neural networks (HGNNs) to …
Meta-learning meets the Internet of Things: Graph prototypical models for sensor-based human activity recognition
With the rapid growth of the Internet of Things (IoT), smart systems and applications are
equipped with an increasing number of wearable sensors and mobile devices. These …
equipped with an increasing number of wearable sensors and mobile devices. These …
Temporal network embedding using graph attention network
A Mohan, KV Pramod - Complex & Intelligent Systems, 2022 - Springer
Graph convolutional network (GCN) has made remarkable progress in learning good
representations from graph-structured data. The layer-wise propagation rule of conventional …
representations from graph-structured data. The layer-wise propagation rule of conventional …
Hopgat: Hop-aware supervision graph attention networks for sparsely labeled graphs
Due to the cost of labeling nodes, classifying a node in a sparsely labeled graph while
maintaining the prediction accuracy deserves attention. The key point is how the algorithm …
maintaining the prediction accuracy deserves attention. The key point is how the algorithm …
Image classification model based on GAT
B Xu, S Ding, Y Zhang - Journal of Physics: Conference Series, 2020 - iopscience.iop.org
In the field of image classification, graph neural network (GNN) is a kind of structured data
modeling architecture with larger functions. However, there are still some problems, such as …
modeling architecture with larger functions. However, there are still some problems, such as …