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 …

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 …

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 …

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 …

Continual learning on graph data, which aims to accommodate new tasks over newly
emerged graph data while maintaining the model performance over existing tasks, is …

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 …

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 …

The ability to incrementally learn new classes is vital to all real-world artificial intelligence
systems. A large portion of high-impact applications like social media, recommendation …

Continual graph learning routinely finds its role in a variety of real-world applications where
the graph data with different tasks come sequentially. Despite the success of prior works, it …