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 …

Personalized recommender systems have been widely studied and deployed to reduce
information overload and satisfy users' diverse needs. However, conventional …

Memory replay, which stores a subset of historical data from previous tasks to replay while
learning new tasks, exhibits state-of-the-art performance for various continual learning …

Graph learning plays a pivotal role and has gained significant attention in various
application scenarios, from social network analysis to recommendation systems, for its …

Incremental learning is a machine learning approach that involves training a model on a
sequence of tasks, rather than all tasks at once. This ability to learn incrementally from a …

Incremental recommendation systems have garnered significant research interest since they
ideally adapt to users' ongoing events (such as clicking, browsing, and reviewing) and …

With the digitization of modern cities, large data volumes and powerful computational
resources facilitate the rapid update of intelligent models deployed in smart cities. Continual …

Recommender systems now consume large-scale data and play a significant role in
improving user experience. Graph Neural Networks (GNNs) have emerged as one of the …

Recently, continual graph learning has been increasingly adopted for diverse graph-
structured data processing tasks in non-stationary environments. Despite its promising …

Graph Neural Networks (GNNs) play a crucial role in various fields. However, most existing
deep graph learning frameworks assume pre-stored static graphs and do not support …