Information Theoretic Prototype Selection for Unattributed Graphs

Lin Han, Luca Rossi, Andrea Torsello, Richard C. Wilson, Edwin R. Hancock

Abstract: In this paper we propose a prototype size selection method for a set of sample graphs. Our first contribution is to show how approximate set coding can be extended from the vector to graph domain. With this framework to hand we show how prototype selection can be posed as optimizing the mutual information between two partitioned sets of sample graphs. We show how the resulting method can be used for prototype graph size selection. In our experiments, we apply our method to a real-world dataset and investigate its performance on prototype size selection tasks.