Detecting Communities and Performing Statistical Inferences on Networks through Renewal Non-backtracking Random Walks

Speaker: Behnaz Moradi, postdoctoral associate researcher at the UVA School of Data Science

Abstract: In this dissertation, Behnaz Moradi, postdoctoral associate researcher at the School of Data Science, develops the renewal non-backtracking random walk (RNBRW), a variant of random walk in which the walk is prohibited from returning back to a node in exactly two steps and terminates and restarts once it completes a loop, as a way of quantifying this cyclic structure. Specifically, she proposes using the retracing probability of an edge, the likelihood that the edge completes a cycle in RNBRW, as a way of quantifying cyclic structure.