Graph-based Relational Learning with Application to Security

Authors:
Lawrence Holder;Diane Cook;Jeff Coble;Maitrayee Mukherjee
Affiliations:
(Correspd.) Department of Computer Science and Engineering University of Texas at Arlington Box 19015, Arlington, TX 76019, USA. holder@cse.uta.edu/ cook@cse.uta.edu/ coble@cse.uta.edu/ mukherje@c ...;Department of Computer Science and Engineering University of Texas at Arlington Box 19015, Arlington, TX 76019, USA. holder@cse.uta.edu/ cook@cse.uta.edu/ coble@cse.uta.edu/ mukherje@cse.uta.edu;Department of Computer Science and Engineering University of Texas at Arlington Box 19015, Arlington, TX 76019, USA. holder@cse.uta.edu/ cook@cse.uta.edu/ coble@cse.uta.edu/ mukherje@cse.uta.edu;Department of Computer Science and Engineering University of Texas at Arlington Box 19015, Arlington, TX 76019, USA. holder@cse.uta.edu/ cook@cse.uta.edu/ coble@cse.uta.edu/ mukherje@cse.uta.edu
Venue:
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Year:
2005

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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.