SEGREGATION IN SOCIAL NETWORKS: MARKOV BRIDGE MODELS AND ESTIMATION

This paper deals with the modeling and estimation of the sociological phenomena called segregation in social networks. Specifically, we present a novel community-based graph model that represent segregation as a Markov bridge process. A Markov bridge is a one-dimensional Markov random field that facilitates modeling the formation and disassociation of communities at deterministic times which is important in social networks with known timed events. Based on the proposed model, we provide Bayesian filtering algorithms for recursively estimating the level of segregation using noisy samples obtained from the graph. Numerical results indicate that the proposed filtering algorithm outperforms the conventional hidden Markov modeling in terms of the mean-squared error. The proposed filtering method is useful in computational social science where data-driven estimation of the level of segregation from noisy data is required.