Maximising influence in sensed heterogeneous social network with privacy preservation Online publication date: Sat, 15-Sep-2018
by Meng Han; Qilong Han; Lijie Li; Ji Li; Yingshu Li
International Journal of Sensor Networks (IJSNET), Vol. 28, No. 2, 2018
Abstract: Maximising influence to improve marketing performance has a significant impact on targeted advertisements and viral product promotion, which has become a fundamental problem in social data analysis. Most existing works neglect the fact that location data could also play an important role in the influence prorogation. This paper considers maximising influence towards both sensed location data and online social data with privacy concern. We merge location data from cyber-physical networks and relationship data from online social networks into a unified, then propose an efficient algorithm to solve the influence maximisation problem. Furthermore, our privacy-preserving mechanism could protect the sensitive location and link information during the whole process of data analysis. Real-life datasets are empirically tested with our framework and demonstrate the power of sensed and online data combination to influence maximisation. The experiment results suggest that our framework is outperforming most existing alternative resolutions and succeeds in preserving privacy.
Online publication date: Sat, 15-Sep-2018
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Sensor Networks (IJSNET):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com