Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

Deep learning has gained superior accuracy on Euclidean structure data in neural networks.
As a result, non-Euclidean structure data, such as graph data, has more sophisticated …

Graph neural networks (GNNs) have recently exploded in popularity thanks to their broad
applicability to graph-related problems such as quantum chemistry, drug discovery, and high …

The current printed circuit board (PCB) defect detection model is difficult to balance accuracy
and computational cost and cannot satisfy the requirements of practical applications. In this …

Temporal Graph Neural Networks are gaining popularity in modeling interactions on
dynamic graphs. Among them, Temporal Graph Attention Networks (TGAT) have gained …

The integration of Artificial Intelligence Internet of Things (AIoT) with consumer electronics
has resulted in enhanced connectivity and intelligence within the consumer electronics …

Graph convolutional networks (GCNs) are becoming increasingly popular as they overcome
the limited applicability of prior neural networks. One recent trend in GCNs is the use of deep …

Deep learning on point clouds has attracted increasing attention for various emerging 3D
computer vision applications, such as autonomous driving, robotics, and virtual reality …

Heterogeneous graph neural networks (HGNNs) have emerged as powerful algorithms for
processing heterogeneous graphs (HetGs), widely used in many critical fields. To capture …

Graph-related applications have experienced significant growth in academia and industry,
driven by the powerful representation capabilities of graph. However, efficiently executing …