  Catastrophic forgetting refers to the tendency that a neural network
"forgets" the previous learned knowledge upon learning new tasks. Prior methods
have been focused on overcoming this problem on convolutional neural networks
(CNNs), where the input samples like images lie in a grid domain, but have
largely overlooked graph neural networks (GNNs) that handle non-grid data. In
this paper, we propose a novel scheme dedicated to overcoming catastrophic
forgetting problem and hence strengthen continual learning in GNNs. At the
heart of our approach is a generic module, termed as topology-aware weight
preserving~(TWP), applicable to arbitrary form of GNNs in a plug-and-play
fashion. Unlike the main stream of CNN-based continual learning methods that
rely on solely slowing down the updates of parameters important to the
downstream task, TWP explicitly explores the local structures of the input
graph, and attempts to stabilize the parameters playing pivotal roles in the
topological aggregation. We evaluate TWP on different GNN backbones over
several datasets, and demonstrate that it yields performances superior to the
state of the art. Code is publicly available at
\url{https://github.com/hhliu79/TWP}.
