Graph-based Relational Learning with Application to Security

Issue title: Advances in Mining Graphs, Trees and Sequences

Article type: Research Article

Authors: Holder, Lawrence | Cook, Diane | Coble, Jeff | Mukherjee, Maitrayee

Affiliations: Department of Computer Science and Engineering University of Texas at Arlington Box 19015, Arlington, TX 76019, USA. [email protected]; [email protected]; [email protected]; [email protected]

Note: [] Address for correspondence: Department of Computer Science and Engineering, University of Texas at Arlington, Box 19015, Arlington, TX 76019, USA

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.

Keywords: Graphs, relational learning, security

Journal: Fundamenta Informaticae, vol. 66, no. 1-2, pp. 83-101, 2005