Graph-based Relational Learning with Application to Security

Abstract

We describe an approach to learning patterns in relational data represented as a graph. The approach, implemented in the Subdue system, searches for patterns that maximally compress the input graph. Subdue can be used for supervised learning, as well as unsupervised pattern discovery and clustering. We apply Subdue in domains related to homeland security and social network analysis.