TOSI: A trust-oriented social influence evaluation method in contextual social networks

Guanfeng Liu*, Feng Zhu, Kai Zheng, An Liu, Zhixu Li, Lei Zhao, Xiaofang Zhou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

Online Social Networks (OSNs) have been used as the means for a variety of applications. For example, social networking platform has been used in employment system, e-Commerce and CRM system to improve the quality of recommendations with the assistance of social networks. In these applications, social influence acts as a significant role, affecting people's decision-making. However, the existing social influence evaluation methods do not fully consider the social contexts, i.e., the social relationships and the social trust between participants, and the preferences of participants, which have significant impact on social influence evaluation in OSNs. Thus, these existing methods cannot deliver accurate social influence evaluation results. In our paper, we propose a Trust-Oriented Social Influence evaluation method, called TOSI, with taking the social contexts into account. We conduct experiments onto two real social network datasets, i.e., Epinions and DBLP. The experimental results illustrate that our TOSI method greatly outperforms the state-of-the-art method SoCap in terms of effectiveness, efficiency and robustness.

Original languageEnglish
Pages (from-to)130-140
Number of pages11
JournalNeurocomputing
Volume210
DOIs
Publication statusPublished - 19 Oct 2016
Externally publishedYes

Keywords

  • social influence
  • social network
  • trust

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