In this set of machine learning tasks on graph data, instead of making predictions over the individual components of a single graph, we are given a dataset of multiple different graphs. In graph classification and regression, the goal is to make independent predictions specific to each graph. On the other hand, in graph clustering, the goal is to learn an unsupervised measure of similarity between pairs of graphs.