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 …

Graph Neural Network (GNN) training and inference involve significant challenges of
scalability with respect to both model sizes and number of layers, resulting in degradation of …

This survey overviews recent Graph Convolutional Networks (GCN) advancements,
highlighting their growing significance across various tasks and applications. It underscores …

Distributed full-graph training of Graph Neural Networks (GNNs) has been widely adopted to
learn large-scale graphs. While recent system advancements can improve the training …

Training graph neural networks (GNNs) on large-scale graph data holds immense promise
for numerous real-world applications but remains a great challenge. Several disk-based …

Graph neural networks (GNNs) have achieved great success on various graph tasks.
However, recent studies have revealed that GNNs are vulnerable to injective attacks. Due to …

Graph Neural Networks (GNNs), which obtain node embeddings by attribute propagates
along graph topology, exhibit significant power in graph-structured data mining. However …

Graph neural networks (GNNs) are one of the most rapidly growing fields within deep
learning. According to the growth in the dataset and the model size used for GNNs, an …

Graphs are essential data structures for modeling complex interactions in domains such as
social networks, molecular structures, and biological systems. Graph-level tasks, which …

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 …