Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces

Understand

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 still faces great challenges.
• On the one hand, existing methods work with the zero-curvature Euclidean space, and largely ignore the fact that curvature varies over the coming graph sequence.
• On the other hand, continual learners in the literature rely on abundant labels, but labeling graph in practice is particularly hard especially for the continuously emerging graphs on-the-fly.