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Investigating Social Networks with Agent Based Simulation and Link Prediction Methods Investigating Social Networks with Agent Based Simulation and Link Prediction Methods Angelico Giovanni Fetta School of Mathematics A thesis presented for the degree of Doctor of Philosophy 2014 Summary Social networks are increasingly being investigated in the context of individual behaviours. Research suggests that friendship connections have the ability to influence individual ac- tions, change personal opinions and subsequently impact upon personal wellbeing. This thesis aims to investigate the effects of social networks, through the use of Agent Based Simulation (ABS) and Link Prediction (LP) methods. Three main investigations form this thesis, culminating in the development of a new simulation-based approach to Link Predic- tion (PageRank-Max) and a model of behavioural spread through a connected population (Behavioural PageRank-Max). The first project investigates the suitability of ABS to explore a connected social system. The Peter Principle is a theory of managerial incompetence, having the potential to cause detrimental effects to system efficiency. Through the investigation of a theoretical hierar- chy of workplace social contacts, it is observed that the structure of a social network has the ability to impact system efficiency, demonstrating the importance of social network structure in conjunction with individual behaviours. The second project aims to further understand the structure of social networks, through the exploration of adolescent offline friendship data, taken from ‘A Stop Smoking in Schools Trial’ (ASSIST). An initial analysis of the data suggests certain factors may be pertinent in the formation of school social networks, identifying the importance of centrality mea- sures. An ABS aiming to predict the evolution of the ASSIST social networks is created, developing an algorithm based upon the optimisation of an individual’s eigen-centrality - termed PageRank-Max. This new approach to Link Prediction is found to predict ASSIST social network evolution more accurately than four existing prominent LP algorithms. The final part of this thesis attempts to improve the PageRank-Max method, by placing par- ticular emphasis upon specific individual attributes. Two new methods are developed, the first restricting the search space of the algorithm (Behavioural Search), while the second al- ters its calculation process by applying specific attribute weights (Behavioural PageRank- Max). The results demonstrate the importance of individual attributes in adolescent friend- ship selection. Furthermore, the Behavioural PageRank-Max offers an approach to model the spread of behaviours in conjunction with social network structure, with the value of this being evaluated against alternative models. i ii Declaration This work has not been submitted in substance for any other degree or award at this or any other university or place of learning, nor is being submitted concurrently in candidature for any degree or other award. Signed .. Date . STATEMENT 1 This thesis is being submitted in partial fulfilment of the requirements for the degree of PhD. Signed .. Date . STATEMENT 2 This thesis is the result of my own investigations, except where otherwise stated. Other sources are acknowledged and explicit references given. A reference section is appended. Signed .. Date . STATEMENT 3 I hereby give consent for my thesis, if accepted, to be available for photocopying and for inter-library loan, and for the title and summary to be made available to outside organisa- tions. Signed .. Date . iii iv Acknowledgements First and foremost, I would like to thank my supervisors - Paul Harper, Janet Williams and Vincent Knight - for the time, energy and passion they have put into this research. Thank you for giving me the freedom to take us off into some (really) unexpected directions, involving jealous co-workers, smoking children and an array of specially selected snacks. I hope to be able to share a whiskey, a gin and tonic, and a cherry coke with all three of you again very soon. I would also like to thank Israel Vieira who was integral in the early stages of my work - giving me the knowledge necessary to become an autonomous agent. Thank you also to the EPSRC and LANCS initiative, who have kept me in food, clothes and copious amounts of olive oil for the last 3.5 years. Thank you to the DECIPHer group, namely Laurence Moore and Jo Holliday, for providing the ASSIST data. Also, a very special thank you to Jonathan Gillard, whose wise words and pastoral care have kept my head out of the shed on a number of occasions. Truth be told, doing a PhD is soul destroying, but I was fortunate to have people around who made coming in everyday worthwhile. From muscling my way into the “Fabulous 4” all those years ago, with the group growing to incorporate special applied mathematicians, the Warwick upper classes and all manner of wonderful characters. So many memories. So much Euphoria! Given that this research is about the effect of social networks, it is only fitting that I mention the impact of my own social network. My parents, who have always pushed me to succeed and supported me; my brothers, for keeping my feet on the ground with their pep-talks laden with sarcasm; my housemate Jo, for putting up with my lack of conversation and inability to deal with tupperware; and those special members of my family, who always welcome me with open arms and an open fridge whenever I decide to drop by. Of course, my social circle would not be complete if I didn’t mention Clare-o, always direct and always up for doing nothing - long may our ‘moments of poetry’ continue. Finally, to my number one ‘Cardiff girl’: Leanne Smith. My biggest discovery during my research wasn’t PageRank-Max, it was the curly haired little lady sitting across from me. Thank you for being my team mate, my sous chef and my reason for bringing two pieces of kitchen roll to the table. Xxxx. v vi Publications and Presentations Publications • Fetta, A. G., Harper, P. R., Knight, V. A., Vieira, I. T. and Williams J. E. (2012). On the Peter Principle: An agent based investigation into the consequential effects of social networks and behavioural factors. Physica A: Statistical Mechanics and its Applications, 391(9): 2898 - 2910. Awards • Early Career Researcher Poster Prize at ORAHS Conference 2012 - First Place. Conference Contributions & Presentations • Fetta, A. G., Harper, P. R., Knight, V. A., Vieira, I. T. and Williams J. E. (2010). Agent Based Simulation for Complex Health Systems Interventions; SWORDS Sem- inar, Cardiff. • Fetta, A. G., Jones, M., Knight, V. A., and Williams J. E. (2010). Workforce Plan- ning: Signing on to system dynamics? System Dynamic Special Interest Group Meeting (2011), Cardiff. • Fetta, A. G., Harper, P. R., Knight, V. A., Vieira, I. T. and Williams J. E. (2011). The Peter Principle, Agents and Beyond. SWORDS Seminar, Cardiff. • Fetta, A. G., Harper, P. R., Knight, V. A., Vieira, I. T. and Williams J. E. (2011), On the Peter Principle: An Agent Based Investigation into the Consequential Effects of Social Networks and Behavioural Factors. Winter Simulation Conference, Pheonix, Arizona. • Fetta, A. G., Harper, P. R., Knight, V. A., Vieira, I. T. and Williams J. E. (2012). On the Peter Principle: An Agent Based Investigation into the Consequential Effects of Social Networks and Behavioural Factors. SCOR, Nottingham. vii • Fetta, A. G., Harper, P. R., Knight, V. A., Vieira, I. T. and Williams J. E. (2012). On the Peter Principle: An Agent Based Investigation into the Consequential Effects of Social Networks and Behavioural Factors. EURO, Vilnuis, Lithuania. • Fetta, A. G., Harper, P. R., Knight, V. A. and Williams J. E. (2012). Modelling Adolescent Smoking Behaviours with Social Network Analysis. ORAHS, Twente, Holland. • Fetta, A. G., Harper, P. R., Knight, V. A. and Williams J. E. (2012). Modelling Adolescent Smoking Behaviours with Social Network Analysis. NHS Confederation Event: Change by design - systems modelling and simulation in healthcare, Exeter. • Fetta, A. G., Harper, P. R., Knight, V. A. and Williams J. E. (2013). Modelling Adolescent Smoking Behaviours with Social Network Analysis. INSNA Sunbelt Conference, Hamburg, Germany. • Fetta, A. G., Harper, P. R., Knight, V. A. and Williams J. E. (2013). Modelling Ado- lescent Smoking Behaviours with Social Network Analysis. EURO, Rome, Italy. viii List of Abbreviations ABS Agent Based Simulation API Application Programming Interface ASSIST AStop Smoking In Schools Trial BPRM Behavioural PageRank-Max CI Confidence Interval CS Common Sense DECIPHer Development and Evaluation of Complex Interventions for Public Health Improvement EOS Evolution of Organised Societies ESS Evolutionary Stable Strategy GUI Graphical User Interface LP Link Prediction MANTA Modelling an ANT hill Activity MAS Multi Agent System MCMC Markov Chain Monte Carlo MLE Maximum Likelihood Estimator MPI Mass Psychogenic Illness NBM Network Behavioural Model NE Network Efficiency OOP Object-Oriented Programming OR Operational Research OSS Open Source System OOP Object-Oriented Programming PP Peter Principle PS Proprietary System PSM Population Structure Model RAN RANdom Network SAB Stochastic Actor Based SF Scale Free Network SNS Social Network Simulation SW Small World Network WHO World Health Organisation ix x Contents 1 Introduction1 1.1 Motivation . .2 1.1.1 Social Connection . .2 1.1.2 Social Network Simulation . .4 1.1.3 Application . .5 1.2 Research Methods . .7 1.3 Research Aims . .8 1.4 Outline . .9 1.5 Chapter Review . 11 2 Simulation Literature Review 13 2.1 What is Simulation? . 14 2.1.1 Benefits of Simulation . 15 2.1.2 Limitations . 17 2.1.3 Building a Simulation . 17 2.2 Types of Simulation . 20 2.2.1 System Dynamics . 20 2.2.2 Discrete Event Simulation . 21 2.2.3 Agent Based Simulation . 22 2.2.4 Advances in Simulation . 31 2.3 Applications of ABS . 32 2.3.1 Common ABS Applications . 32 2.3.2 Social Theory . 34 2.3.3 Social Networks .
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