Abstract

In this paper we differentiate between hard and soft label propagation for classification of relational (networked) data. The latter method assigns probabilities or class-membership scores to data instances, then propagates these scores throughout the networked data, whereas the former works by explicitly propagating class labels at each iteration. We present a comparative empirical study of these methods applied to a relational binary classification task, and evaluate two approaches on both synthetic and real–world relational data. Our results indicate that while neither approach dominates the other over the entire range of input data parameters, there are some interesting and non–trivial tradeoffs between them.

Date

June 19, 2007

Authors

Aram Galstyan, Paul R Cohen

Book

International Conference on Inductive Logic Programming

Pages

98-111

Publisher

Springer Berlin Heidelberg