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 …

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

The efficiency and scalability of graph convolution networks (GCNs) in training
recommender systems (RecSys) have been persistent concerns, hindering their deployment …

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 …

Transformers have recently emerged as powerful neural networks for graph learning,
showcasing state-of-the-art performance on several graph property prediction tasks …