Graph characterisation using graphlet-based entropies

In this paper, we present a general framework to estimate the network entropy that is represented by means of an undirected graph and subsequently employ for classification tasks. The proposed based on local information functionals which are defined using induced connected subgraphs different sizes. These termed graphlets. Specifically, extract set all graphlets specific sizes compute our framework. To classify into categories, construct feature vector whose components obtained computing entropies graphlet We apply two tasks, namely view-based object recognition biomedical datasets with binary outcomes classification. Finally, report compare accuracies method against some state-of-the-art methods.