document: Learning patterns in the dynamics of biological networks Our dynamic graph-based relational mining approach has been developed to learn structural patterns in biological networks as they change over time . The analysis of dynamic networks is important not only to understand life at the system-level , but also to discover novel patterns in other structural data . Most current graph-based data mining approaches overlook dynamic features of biological networks , because they are focused on only static graphs . Our approach analyzes a sequence of graphs and discovers rules that capture the changes that occur between pairs of graphs in the sequence . These rules represent the graph rewrite rules that the first graph must go through to be isomorphic to the second graph . Then , our approach feeds the graph rewrite rules into a machine learning system that learns general transformation rules describing the types of changes that occur for a class of dynamic biological networks . The discovered graph-rewriting rules show how biological networks change over time , and the transformation rules show the repeated patterns in the structural changes . In this paper , we apply our approach to biological networks to evaluate our approach and to understand how the biosystems change over time . We evaluate our results using coverage and prediction metrics , and compare to biological literature .
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