Before revision:  However, it is not yet clear how to efficiently train GNNs on temporal graphs , where new vertices, edges, and even classes appear over time. We face two challenges : First, shifts in the label distribution (including the appearance of new labels), which require adapting the model .
After revision:  However, real-world graphs are often evolving over time and even new classes may arise. We model these challenges as an instance of lifelong learning, in which a learner faces a sequence of tasks and may take over knowledge acquired in past tasks. Such knowledge may be stored explicitly as historic data or implicitly within model parameters. In this work, we systematically analyze the influence of implicit and explicit knowledge. Therefore, we present an incremental training method for lifelong learning on graphs and introduce a new measure based on k-neighborhood time differences to address variances in the historic data. We apply our training method to five representative GNN architectures and evaluate them on three new lifelong node classification datasets .