Many real-world systems are naturally represented as graphs or networks, and a variety of techniques and measures exist for their analysis. However, studies of networks typically employ only a small, largely arbitrary subset of these, and the lack of a systematic comparison makes it unclear which metrics are redundant or complementary. We present a framework for systematic analysis of networks and network metrics, and use it to analyse a large and diverse set of real networks, along with several kinds of synthetic model-generated networks, making use of nearly four hundred network metrics or summary statistics thereof. We demonstrate the utility of the framework for finding redundant metrics, fitting models to real networks, classification of networks, studying evolving networks, relating network features to evolutionary phylogenies, and determining the robustness of metrics to network damage and sampling effects.
