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Small and Large Scale Network Features

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STSW02 - Statistics of geometric features and new data types

Comparing and contrasting networks is hindered by their strongly non-Euclidean structure. I will discuss how one determines “optimal” features to compare two different networks of different sparsity and size. As the topology of any complex system is key to understanding its structure and function, the result will be developed from topological ideas. Fundamentally, algebraic topology guarantees that any system represented by a network can be understood through its closed paths. The length of each path provides a notion of scale, which is vitally important in characterizing dominant modes of system behavior. Here, by combining topology with scale, we prove the existence of universal features which reveal the dominant scales of any network. We use these features to compare several canonical network types in the context of a social media discussion which evolves through the sharing of rumors, leaks and other news. Our analysis enables for the first time a universal understanding of the balance between loops and tree-like structure across network scales, and an assessment of how this balance interacts with the spreading of information online. Crucially, our results allow networks to be quantified and compared in a purely model-free way that is theoretically sound, fully automated, and inherently scalable.



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