Modelling influence in a social network: Metrics and evaluation
Social recommender systems are a recently introduced type of decision support system. One of the issues to be resolved in social recommender systems is the identification of opinion leaders in a network. The focus of this paper is the analysis of a network based on the interactions between users called behavioral analysis. The hypothesis explored in this paper is that Influence Rank can be quantified based on the interaction between users and their behavior. The Influence Rank for a node is defined as the average Influence Rank of its neighborhoods combined with another index called Magnitude of Influence. The correlation between the proposed indices is analyzed in this paper. This combined measure is calculated by a recursive algorithm whose calculation complexity is non-polynomial. However, this measure can be estimated by using the PageRank algorithm. Results supporting the utility of the measure and the accuracy of its estimation using the PageRank approximation are presented.
|Keywords||Behavioural analysis, Modeling, Social influence, Social network|
|Conference||2011 IEEE International Conference on Privacy, Security, Risk and Trust, PASSAT 2011 and 2011 IEEE International Conference on Social Computing, SocialCom 2011|
Hajian, B. (Behnam), & White, A. (2011). Modelling influence in a social network: Metrics and evaluation. In Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011 (pp. 497–500). doi:10.1109/PASSAT/SocialCom.2011.118