  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. In this paper, we propose Ada-DyGNN: a robust knowledge
Adaptation framework via reinforcement learning for Dynamic Graph Neural
Networks. In contrast to previous approaches, which update the embeddings of
the neighbor nodes immediately after adding a new link, Ada-DyGNN adaptively
determines which nodes should be updated. Considering that the decision to
update the embedding of one neighbor node can significantly impact other
neighbor nodes, we conceptualize the node update selection as a sequence
decision problem and employ reinforcement learning to address it effectively.
By this means, we can adaptively propagate knowledge to other nodes for
learning robust node embedding representations. To the best of our knowledge,
our approach constitutes the first attempt to explore robust knowledge
adaptation via reinforcement learning specifically tailored for dynamic graph
neural networks. Extensive experiments on three benchmark datasets demonstrate
that Ada-DyGNN achieves the state-of-the-art performance. In addition, we
conduct experiments by introducing different degrees of noise into the dataset,
quantitatively and qualitatively illustrating the robustness of Ada-DyGNN.
