Graph neural networks (GNNs) are a type of deep learning models that are trained on
graphs and have been successfully applied in various domains. Despite the effectiveness of …

Many Graph Neural Network (GNN) training systems have emerged recently to support
efficient GNN training. Since GNNs embody complex data dependencies between training …

Graph neural networks (GNNs) have significant advantages in dealing with non-Euclidean
data and have been widely used in various fields. However, most of the existing GNN …

Tiered memory, combining multiple memory components with different performance and
capacity, provides a cost-effective solution to increase memory capacity and improve …

Abstract Knowledge Graph (KG) is an essential research field in graph theory, but its
inherent incompleteness and sparsity influence its performance in several fields. Knowledge …

Recent years have witnessed a thriving growth of computing facilities connected at the
network edge, cultivating edge networks as a fundamental infrastructure for supporting …

Graph Neural Networks (GNNs) have demonstrated outstanding results in many graph-
based deep-learning tasks. However, training GNNs on a large graph can be difficult due to …

Graph processing is widely used for many modern applications, such as social networks,
recommendation systems, and knowledge graphs. However, processing large-scale graphs …

Articular cartilage (AC) is essential for minimizing friction in the human knee, but its healthy
function is highly influenced by biomechanical factors such as weight bearing. Non-invasive …

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