Message-passing approach for threshold models of behavior in networks

Authored by Munik Shrestha, Cristopher Moore

Date Published: 2014-02-18

DOI: 10.1103/physreve.89.022805

Sponsors: United States Air Force United States Defense Advanced Research Planning Agency (DARPA) United States National Institutes of Health (NIH)

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

We study a simple model of how social behaviors, like trends and opinions, propagate in networks where individuals adopt the trend when they are informed by threshold T neighbors who are adopters. Using a dynamic message-passing algorithm, we develop a tractable and computationally efficient method that provides complete time evolution of each individual's probability of adopting the trend or of the frequency of adopters and nonadopters in any arbitrary networks. We validate the method by comparing it with Monte Carlo-based agent simulation in real and synthetic networks and provide an exact analytic scheme for large random networks, where simulation results match well. Our approach is general enough to incorporate non-Markovian processes and to include heterogeneous thresholds and thus can be applied to explore rich sets of complex heterogeneous agent-based models.

Tags