Complex network theory has been used, during the last decade, to understand the structures behind complex biological problems, yielding new knowledge in a large number of situations. Nevertheless, such knowledge has remained mostly qualitative. In this contribution, I show how information extracted from a network representation can be used in a quantitative way, to improve the score of a classification task. As a test bed, I consider a dataset corresponding to patients suffering from prostate cancer, and the task of successfully prognosing their survival. When information from a complex network representation is added on top of a simple classification model, the error is reduced from 27.9% to 23.8%. This confirms that network theory can be used to synthesize information that may not readily be accessible by standard data mining algorithms.
Complex Network Representation of the Structure-Mechanical Property Relationships in Elastomers with Heterogeneous Connectivity