Title: Data-Free Continual Graph Learning . Abstract: Graph Neural Networks (GNNs), which effectively learn from static graph-structured data become ineffective when directly applied to streaming data in a continual learning (CL) scenario. A few recent works study this so-called “catastrophic forgetting” problem in GNNs, where historical data are not available during the training stage. However, they make a strong assumption that full access of historical data is provided during the inference stage. This assumption could make the graph learning system impractical to deploy due to a number of reasons, such as limited storage, GDPR1 data retention policy, to name a few. In this work, we study continual graph learning without this strong assumption. Moreover, in practical continual learning, models are sometimes trained with accumulated batch data but required to do on-the-fly inference with a stream of test samples. In this case, without being re-inserted into previous training graphs for inference, streaming test nodes are often very sparsely connected. It makes the inference more difficult as the model is trained on a much more dense graph while required to infer on a sparse graph with insufficient neighborhood information. We propose a simple Replay GNN (ReGNN) to jointly solve the above two challenges without memory buffers (i.e., data-free): catastrophic forgetting and poor neighbour information during inference. Extensive experiments demonstrate the effectiveness of our model over baseline models, including competitive baselines with memory buffers.