Robust Knowledge Adaptation for Dynamic Graph Neural Networks

Understand

Graph structured data often possess dynamic characters in nature.

• Recent years have witnessed the increasing attentions paid to dynamic graph neural networks for modelling graph data.
• However, almost all existing approaches operate under the assumption that, upon the establishment of a new link, the embeddings of the neighboring nodes should undergo updates to learn temporal dynamics.
• Nevertheless, these approaches face the following limitation: If the node introduced by a new connection contains noisy information, propagating its knowledge to other nodes becomes unreliable and may even lead to the collapse of the model.