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A Graph Distance for the Structural Analysis of Dynamic Networks

听 To quantify the evolution of time-varying networks, and detect abnormal behavior, one needs a notion of temporal difference that captures significant organizational changes between two successive instants.

听 We propose a family of distances to quantify structural changes occurring on a graph at different scales.听 We design a randomized algorithm, which scales nearly linearly in the number of edges, to compute an approximation to this novel graph distance.

听 We demonstrate that temporal changes in this graph distance can be used to detect configurational changes that are directly related to the hidden variables governing the evolution of dynamic networks.

听 This is work in collaboration with Dr. Nathan Monnig.