Continual learning (CL) aims to empower machine learning models to learn continually from
new data, while building upon previously acquired knowledge without forgetting. As …

When handling streaming graphs, existing graph representation learning models encounter
a catastrophic forgetting problem, where previously learned knowledge of these models is …

Experience Replay (ER) methods in graph continual learning (GCL) mitigate catastrophic
forgetting by storing and replaying historical tasks. However, these methods often struggle …

Graph lifelong learning (GLL), also known as graph continual or incremental learning,
focuses on adapting to new tasks presented by emerging graph data while preserving the …

In the era of big data, managing evolving graph data poses substantial challenges due to
storage costs and privacy issues. Training graph neural networks (GNNs) on such evolving …

Graph class incremental learning (GCIL) requires the model to classify emerging nodes of
new classes while remembering old classes. Existing methods are designed to preserve …