While graph neural networks have made impressive progress in classification and regression on graphs, few approaches to date perform time series prediction on graphs and those that do are mostly limited to edge changes. We suggest that graph edits are a more natural interface for graph-to-graph learning. In particular, graph edits are general enough to describe any graph-to-graph change, not only edge changes, they are sparse, making them easier to understand for humans and more efficient computationally, and they are local, avoiding the need for pooling layers in graph neural networks. In this paper, we propose a simple linear layer - the graph edit network - which takes node embeddings as input and generates a sequence of graph edits that transform the input graph to the output graph. Theoretically, we show that a mapping between the node sets of two graphs is sufficient to construct training data for a graph edit network and that an optimal mapping yields edit scripts that are almost as short as the graph edit distance between the graphs. We further provide a proof-of-concept empirical evaluation on several graph dynamical systems, which are difficult to learn for baselines from the literature.