Unsupervised and supervised learning of graph domains

Abstract

In this chapter, we will describe a method for extracting an underlying graph structure from an unstructured text document. The resulting graph structure is a symmetrical un-directed graph. An unsupervised learning approach is applied to cluster a given text corpus into groups of similar structured graphs. Moreover, if labels are given to some of the documents in the text corpus, a supervised learning approach can be applied to learn the underlying input-output mapping between the symmetrical un-directed graph structures and a real-valued vector. The approach will be illustrated using a standard benchmark problem in text processing, viz., a subset of the Reuters text corpus. Some observations and further research directions are given. © 2009 Springer-Verlag Berlin Heidelberg.