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.

翻译：图结构数据本质上常具有动态特性。近年来，动态图神经网络在图形数据建模领域受到日益广泛的关注。然而，几乎所有现有方法都基于一个隐含假设：当新链接建立时，相邻节点的嵌入表示应同步更新以学习时序动态性。但这类方法存在以下局限：若新连接引入的节点包含噪声信息，将其知识传播至其他节点将变得不可靠，甚至可能导致模型崩溃。本文提出Ada-DyGNN——一种基于强化学习的动态图神经网络鲁棒知识自适应框架。与以往在添加新链接后立即更新邻居节点嵌入的方法不同，Ada-DyGNN可自适应判定应更新哪些节点。考虑到对某一邻居节点嵌入的更新决策会显著影响其他邻居节点，我们将节点更新选择建模为序列决策问题，并采用强化学习进行有效求解。通过这种方式，我们能够自适应地向其他节点传播知识，从而学习鲁棒的节点嵌入表示。据我们所知，本文首次探索了针对动态图神经网络的强化学习鲁棒知识自适应方法。在三个基准数据集上的大量实验表明，Ada-DyGNN达到了最优性能。此外，我们在数据集中引入不同程度噪声进行实验，从定量和定性两个角度验证了Ada-DyGNN的鲁棒性。