Ranking and semi-supervised classification on large scale graphs using map-reduce

Abstract

Label Propagation, a standard algorithm for semi-supervised classification, suffers from scalability issues involving memory and computation when used with large-scale graphs from real-world datasets. In this paper we approach Label Propagation as solution to a system of linear equations which can be implemented as a scalable parallel algorithm using the map-reduce framework. In addition to semi-supervised classification, this approach to Label Propagation allows us to adapt the algorithm to make it usable for ranking on graphs and derive the theoretical connection between Label Propagation and PageRank. We provide empirical evidence to that effect using two natural language tasks -- lexical relat-edness and polarity induction. The version of the Label Propagation algorithm presented here scales linearly in the size of the data with a constant main memory requirement, in contrast to the quadratic cost of both in traditional approaches.