Learning representations on large-sized graphs is a long-standing challenge due to the inter-
dependence nature involved in massive data points. Transformers, as an emerging class of …

Graph neural networks (GNNs), as the de-facto model class for representation learning on
graphs, are built upon the multi-layer perceptrons (MLP) architecture with additional …

In recent years, Graph Neural Networks (GNNs) have become the de facto tool for learning
node and graph representations. Most GNNs typically consist of a sequence of …

In this paper, we present LiGNN, a deployed large-scale Graph Neural Networks (GNNs)
Framework. We share our insight on developing and deployment of GNNs at large scale at …

Effective multi-intersection collaboration is pivotal for reinforcement-learning-based traffic
signal control to alleviate congestion. Existing work mainly chooses neighboring …

Abstract While Graph Neural Networks (GNNs) have demonstrated their effectiveness in
processing non-Euclidean structured data, the neighborhood fetching of GNNs is time …

Despite Graph Neural Networks (GNNs) have achieved prominent success in many graph-
based learning problem, such as credit risk assessment in financial networks and fake news …

Self-supervised learning on graphs has made large strides in achieving great performance
in various downstream tasks. However, many state-of-the-art methods suffer from a number …

Graph Neural Networks (GNNs) are prominent in graph machine learning and have shown
state-of-the-art performance in Link Prediction (LP) tasks. Nonetheless, recent studies show …

Abstract Graph Neural Networks (GNNs) have displayed considerable promise in graph
representation learning across various applications. The core learning process requires the …