Bayesian modeling and uncertainty quantification for descriptive social networks.

ABSTRACT: This article presents a simple and easily implementable Bayesian approach to model and quantify uncertainty in small descriptive social networks. While statistical methods for analyzing networks have seen burgeoning activity over the last decade or so, ranging from social sciences to genetics, such methods usually involve sophisticated stochastic models whose estimation requires substantial structure and information in the networks. At the other end of the analytic spectrum, there are purely descriptive methods based upon quantities and axioms in computational graph theory. In social networks, popular descriptive measures include, but are not limited to, the so called Krackhardt's axioms. Another approach, recently gaining attention, is the use of PageRank algorithms. While these descripti

SUBMITTER: Nemmers T 

PROVIDER: S-EPMC6335039 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature