A Context-Aware Trust-Oriented Influencers Finding in Online Social Networks

Abstract

Online Social Networks (OSNs) have been used as the means for a variety of applications, like employment system, e-Commerce and CRM system. 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, like 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 Context-Aware Trust-Oriented Influencers Finding method, called CT-Influence, with social contexts taken into account. We conduct experiments onto two real social network datasets, i.e., Epinions and DBLP. The experimental results illustrate that our CT-Influence method greatly outperforms the state-of-the-art method So Cap in terms of effectiveness and efficiency.