Graph learning is a popular approach for perfor ming machine learning on graph-structured
data. It has revolutionized the machine learning ability to model graph data to address …

There has been a lot of activity in graph representation learning in recent years. Graph
representation learning aims to produce graph representation vectors to represent the …

In recent years, prompt tuning has sparked a research surge in adapting pre-trained models.
Unlike the unified pre-training strategy employed in the language field, the graph field …

Graph condensation, which reduces the size of a large-scale graph by synthesizing a small-
scale condensed graph as its substitution, has immediate benefits for various graph learning …

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 …

Over the past decades, the abundance of personal data has led to the rapid development of
machine learning models and important advances in artificial intelligence (AI). However …

Continual graph learning (CGL) is purposed to continuously update a graph model with
graph data being fed in a streaming manner. Since the model easily forgets previously …

Many real-world graph learning tasks require handling dynamic graphs where new nodes
and edges emerge. Dynamic graph learning methods commonly suffer from the catastrophic …

Recently, spatiotemporal graph convolutional networks are becoming popular in the field of
traffic flow prediction and significantly improve prediction accuracy. However, the majority of …

With the progress of urban transportation systems, a significant amount of high-quality traffic
data is continuously collected through streaming manners, which has propelled the …