Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

Deep learning's performance has been extensively recognized recently. Graph neural
networks (GNNs) are designed to deal with graph-structural data that classical deep …

Graph neural networks (GNNs) are emerging for machine learning research on graph-
structured data. GNNs achieve state-of-the-art performance on many tasks, but they face …

Graph Neural Networks (GNNs) have demonstrated effectiveness in various graph-based
tasks. However, their inefficiency in training and inference presents challenges for scaling …

Semi-supervised learning is an effective method to study how to use both labeled data and
unlabeled data to improve the performance of the classifier, which has become the hot field …

Graph Neural Networks (GNNs) have gained significant attention in recent years due to their
ability to learn representations of graph structured data. Two common methods for training …

As a new algorithm of graph embedding, graph neural networks (GNNs) have been widely
used in many fields. However, GNN computing has the characteristics of both sparse graph …

Due to the neighborhood explosion phenomenon, scaling up graph neural networks to large
graphs remains a huge challenge. Various sampling-based mini-batch approaches, such as …

Recently, graph neural networks (GNNs) have achieved excellent performance on many
graph-related tasks. Existing typical GNNs follow the neighborhood aggregation strategy …

Graph Neural Networks (GNNs) are powerful tools for graph representation learning, but
they face challenges when applied to large-scale graphs due to substantial computational …