Joint estimation of preferential attachment and node fitness in growing complex networks.

ABSTRACT: Complex network growth across diverse fields of science is hypothesized to be driven in the main by a combination of preferential attachment and node fitness processes. For measuring the respective influences of these processes, previous approaches make strong and untested assumptions on the functional forms of either the preferential attachment function or fitness function or both. We introduce a Bayesian statistical method called PAFit to estimate preferential attachment and node fitness without imposing such functional constraints that works by maximizing a log-likelihood function with suitably added regularization terms. We use PAFit to investigate the interplay between preferential attachment and node fitness processes in a Facebook wall-post network. While we uncover evidence for bot

SUBMITTER: Pham T 

PROVIDER: S-EPMC5013469 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature