Abstract:Objective 
Image texture features, such as those derived by Haralick et al., are a powerful metric for image classification and
are used across fields including cancer research. Our aim is to demonstrate how analogous texture features can be derived for graphs and networks. We also aim to illustrate how these new metrics summarize graphs, may aid comparative graph studies, may help classify biological graphs, and might assist in detecting dysregulation in cancer.

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