The efi??cient annotation of documents in vast corpora calls for scalable methods of text classii??cation. Representing the documents in the form of graph vertices, rather than in the form of vectors in a bag of words space, allows for the necessary information to be pre-computed and stored. It also fundamentally changes the problem dei??nition, from a content-based to a relation-based classii??cation problem. Efi??ciently creating a graph where nearby documents are likely to have the same annotation is the central task of this paper. We compare the effectiveness of various approaches to graph construction by building graphs of 800,000 vertices based on the Reuters corpus, showing that relation-based classii??cation is competitive with Support Vector Machines, which can be considered as state of the art. We further show that the combination of our relation-based approach and Support Vector Machines leads to an improvement over the methods individually.