Graph-Based Data Mining in Dynamic Networks: Empirical Comparison of Compression-Based and Frequency-Based Subgraph Mining

Abstract

We propose a dynamic graph-based relational mining approach using graph-rewriting rules to learns patterns in networks that structurally change over time. A dynamic graph containing a sequence of graphs over time represents dynamic properties as well as structural properties of the network. Our approach discovers graph-rewriting rules, which describe the structural transformations between two sequential graphs over time, and also learns description rules that generalize over the discovered graph-rewriting rules. The discovered graph-rewriting rules show how networks change over time, and the description rules in the graph-rewriting rules show temporal patterns in the structural changes. We apply our approach to biological networks to understand how the biosystems change over time. Our compression-based discovery of the description rules is compared with the frequent subgraph mining approach using several evaluation metrics.