Improving Learning in Networked Data by Combining Explicit and Mined
in Proceedings of the Twenty-Second Conference on Artificial Intelligence (AAAI-2007), July 22-26, 2007, Vancouver, Canada.

This paper is about using multiple types of information for classification of networked data in a semi-supervised setting: given a fully described network (nodes and edges) with known labels for some of the nodes, predict the labels of the remaining nodes. One method recently developed for doing such inference is a guilt-by-association model. This method has been independently developed in two different settings--relational learning and semi-supervised learning. In relational learning, the setting assumes that the networked data has explicit links such as hyperlinks between web-pages or citations between research papers. The semi-supervised setting assumes a corpus of non-relational data and creates links based on similarity measures between the instances. Both use only the known labels in the network to predict the remaining labels but use very different information sources. The thesis of this paper is that if we combine these two types of links, the resulting network will carry more information than either type of link by itself. We test this thesis on six benchmark data sets, using a within-network learning algorithm, where we show that we gain significant improvements in predictive performance by combining the links. We describe a principled way of combining multiple types of edges with different edge-weights and semantics using an objective graph measure called node-based assortativity. We investigate the use of this measure to combine text-mined links with explicit links and show that using our approach significantly improves performance of our classifier over naively combining these two types of links.