Classification in Graphs using Discriminative Random Walks : Semi-supervised learning, large graphs, betweenness measure, passage times

This paper describes a novel technique, called D-walks, to tackle semi-supervised classification problems in large graphs. We introduce here a betweenness measure based on passage times during random walks of bounded lengths in the input graph. The class of unlabeled nodes is predicted by maximizing the betweenness with labeled nodes. This approach can deal with directed or undirected graphs with a linear time complexity with respect to the number of edges, the maximum walk length considered and the number of classes. Preliminary experiments on the CORA database show that D-walks outperforms NetKit (Macskassy & Provost, 2007) as well as Zhou et al’s algorithm (Zhou et al.,2005), both in classification rate and computing time.