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.

Highlights

  • Investigation into individual behaviours in relation to social networks has experienced substantial growth in recent years

  • We propose a new link prediction algorithm, the PageRank-Max (PR-Max) method, which provides an individual perspective of centrality, a searching agent altering its connections based upon the personal optimisation of its own eigen-centrality

  • The previous sections have described the creation of an Agent Based Simulation (ABS) to predict social network evolution implementing five separate link

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Summary

Introduction

Investigation into individual behaviours in relation to social networks has experienced substantial growth in recent years. This is in part due to the availability of social network data as a result of social networking sites such as Facebook, Twitter and Google+, and the computing advancements that allow for the exploration of such large data sets (Kwak, Lee, Park, & Moon, 2010; Mislove, Koppula, Gummadi, Druschel, & Bhattacharjee, 2008; SalterTownshend, 2012). This paper is concerned with the individual decisions that cause social network evolution in adolescents, which is applied to data from a large smoking cessation programme in secondary schools. Smoking increases the risk for serious health problems, many diseases, and death (Centers for Disease Control & Prevention, 2014)

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