Predicting adolescent social networks to stop smoking in secondary schools

Authored by Angelico Fetta, Paul Harper, Vincent Knight, Janet Williams

Date Published: 2018

DOI: 10.1016/j.ejor.2017.07.039

Sponsors: United Kingdom Engineering and Physical Sciences Research Council (EPSRC)

Platforms: AnyLogic

Model Documentation: ODD Flow charts

Model Code URLs: Model code not found

Abstract

Social networks are increasingly being investigated in the context of individual behaviours. Research suggests that friendship connections have the ability to influence individual actions, change personal opinions and subsequently impact upon personal wellbeing. This paper explores the effect of individual friendship selection decisions, and the impact they may have on the overall evolution of a social network. Using data from a large smoking cessation programme in secondary schools, an agent based simulation aiming to predict the evolution of the adolescent social networks is created. The simulation uses existing friendship selection algorithms from link prediction literature, along with a new approach to link prediction, termed PageRank-Max. This new algorithm is based upon the optimisation of an individuals eigen-centrality, and is found to be more successful than existing methods at predicting the future state of an adolescent social network. This research highlights the importance of eigen-centrality in adolescent friendship decisions, and the use of agent-based simulation to conduct behavioural investigations. Furthermore, it provides a proof-of-concept for targeted interventions driven by social network analysis, demonstrating the utility of using emerging sources of social network data for public heath interventions such as with tobacco use which is a major global health challenge. (C) 2017 The Author(s). Published by Elsevier B.V.

Tags

Simulation Agent-based models Complex networks Social networks systems PageRank Decisions Health-care Or in health services Behavioural or Agent based simulation Link prediction Link-prediction Search engine Web