Peter Ronhovde

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We apply a replica inference based Potts model method to unsupervised image segmentation on multiple scales. This approach was inspired by the statistical mechanics problem of " community detection " and its phase diagram. Specifically, the problem is cast as identifying tightly bound clusters (" communities " or " solutes ") against a background or "(More)
—We present a physics inspired heuristic method for solving combinatorial optimization problems. Our approach is specifically motivated by the desire to avoid trapping in metastable local minima-a common occurrence in hard problems with multiple extrema. Our method involves (i) coupling otherwise independent simulations of a system (" replicas ") via(More)
Community detection in networks refers to the process of seeking strongly internally connected groups of nodes which are weakly externally connected. In this work, we introduce and study a community definition based on internal edge density. Beginning with the simple concept that edge density equals number of edges divided by maximal number of edges, we(More)
We derive rigorous bounds for well-defined community structure in complex networks for a stochas-tic block model (SBM) benchmark. In particular, we analyze the effect of inter-community " noise " (inter-community edges) on any " community detection " algorithm's ability to correctly group nodes assigned to a planted partition, a problem which has been(More)
We survey the application of a relatively new branch of statistical physics— " community detection " – to data mining. In particular, we focus on the diagnosis of materials and automated image segmentation. Community detection describes the quest of partitioning a complex system involving many elements into optimally decoupled subsets or communities of such(More)
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