Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.733382
Title: Structure based online social network link prediction study
Author: Gao, Fei
ISNI:       0000 0004 6497 9609
Awarding Body: King's College London
Current Institution: King's College London (University of London)
Date of Award: 2017
Availability of Full Text:
Access from EThOS:
Access from Institution:
Abstract:
This thesis shed light on the Internet-based social network link prediction problem. After reviewing recent research achievements in this area, two hypotheses are introduced: (i) The performance of topology-based network prediction methods and the characteristics of the networks are correlated. (ii) As networks are dynamic, the performance of prediction can be improved by providing different treatment to different nodes and links. To verify the Hypothesis (i), we conduct experiments with six selected online social networks. The correlation coefficients are calculated between six common network metrics and ten widely used topology-based network link prediction methods. The results show a strong correlation between Gini Coefficient and Preferential Attachment method. This study also reveals two types of networks: prediction-friendly network, for which most of the selected prediction methods perform well with an AUC result above 0.8, and prediction unfriendly network that on the contrary. For Hypothesis (ii), we proposed two network prediction models, the Hybrid Prediction Model and Community Bridge Boosting Prediction Model (CBBPM). The hybrid prediction model assumes network links are formed following different rules. The model linearly combines eight link prediction methods and the evolvement rules have been probed by finding the best weight for each of the methods by solving the linear optimization problem. This experiment result shows an improvement of prediction accuracy. This model takes link prediction as a time series problem. Different from Hybrid Prediction Model, CBBPM provides a different treatment on nodes. We define and classify network nodes as community bridge nodes in a novel approach based on their degree and links position in network communities. The similarity score that is calculated from the selected prediction methods is then boosted for predicting new links. The results from this model also show an enhancement of prediction accuracy. The two hypotheses are validated using the research experiments.
Supervisor: Cooper, Colin Desmond ; Tsoka, Sophia ; McBurney, Peter John Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.733382  DOI: Not available
Share: