Recently, Transformer model, which has achieved great success in many artificial
intelligence fields, has demonstrated its great potential in modeling graph-structured data …

Rapid advances in artificial intelligence (AI) in the last decade have been largely built upon
the wide applications of deep learning (DL). However, the high carbon footprint yielded by …

Graph neural networks, a powerful deep learning tool to model graph-structured data, have
demonstrated remarkable performance on numerous graph learning tasks. To address the …

Graph neural network (GNN)'s success in graph classification is closely related to the
Weisfeiler-Lehman (1-WL) algorithm. By iteratively aggregating neighboring node features …

In recent years, algorithms and neural architectures based on the Weisfeiler-Leman
algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a …

Abstract Graph Neural Networks (GNNs) have achieved remarkable performance on graph-
based tasks. The key idea for GNNs is to obtain informative representation through …

We present GNNAutoScale (GAS), a framework for scaling arbitrary message-passing GNNs
to large graphs. GAS prunes entire sub-trees of the computation graph by utilizing historical …

Graph self-supervised learning (SSL), including contrastive and generative approaches,
offers great potential to address the fundamental challenge of label scarcity in real-world …

We propose GNNLab, a sample-based GNN training system in a single machine multi-GPU
setup. GNNLab adopts a factored design for multiple GPUs, where each GPU is dedicated to …

Graph neural networks have emerged as a leading architecture for many graph-level tasks,
such as graph classification and graph generation. As an essential component of the …