Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.611046
Title: Investigating social networks with Agent Based Simulation and Link Prediction methods
Author: Fetta, Angelico Giovanni
ISNI:       0000 0004 5365 2109
Awarding Body: Cardiff University
Current Institution: Cardiff University
Date of Award: 2014
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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 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 Prediction (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 hierarchy 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 measures. 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 particular emphasis upon specific individual attributes. Two new methods are developed, the first restricting the search space of the algorithm (Behavioural Search), while the second alters its calculation process by applying specific attribute weights (Behavioural PageRank-Max). The results demonstrate the importance of individual attributes in adolescent friendship 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.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.611046  DOI: Not available
Keywords: QA Mathematics
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