Continual learning on graph data has recently attracted paramount attention for its aim to
resolve the catastrophic forgetting problem on existing tasks while adapting the sequentially …

Multimodal data pervades various domains, including healthcare, social media, and
transportation, where multimodal graphs play a pivotal role. Machine learning on multimodal …

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 …

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 …

The existing graph neural architecture search (GNAS) methods assume that the graph tasks
are static during the search process, ignoring the ubiquitous scenarios where sequential …

Graph neural architecture search (GraphNAS) has recently aroused considerable attention
in both academia and industry. However, two key challenges seriously hinder the further …

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 …

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 …

In the realm of personalized recommender systems, the challenge of adapting to evolving
user preferences and the continuous influx of new users and items is paramount …