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 …

Meta-learning meets the Internet of Things: Graph prototypical models for sensor-based human activity recognition

W Zheng, L Yan, C Gou, FY Wang - Information Fusion, 2022 - Elsevier
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 …

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 …

Hopgat: Hop-aware supervision graph attention networks for sparsely labeled graphs

C Ji, R Wang, R Zhu, Y Cai, H Wu - arxiv preprint arxiv:2004.04333, 2020 - arxiv.org
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 …

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 …