A quantitative methodology based on component analysis to identify key users in social networks. (1st January 2012)

• Record Type: Journal Article
• Authors: Canali, Claudia; Lancellotti, Riccardo
• Is Part Of: International journal of social network mining. Volume 1:Number 1(2012)
• Page Start: 27
• Page End: 50
• Publication Date: 2012-01-01
• Subjects: social network analysis -- key user identification -- content dissemination -- principal component analysis -- PCA; Online social networks -- Periodicals; Social networks -- Computer network resources -- Periodicals; Data mining -- Periodicals; 302.30285
• Languages: English
• ISSNs: 1757-8485

Abstract

Social networks are gaining an increasing popularity on the internet and are becoming a critical media for business and marketing. Hence, it is important to identify the key users that may play a critical role as sources or targets of content dissemination. Existing approaches rely only on users social connections; however, considering a single kind of information does not guarantee satisfactory results for the identification of the key users. On the other hand, considering every possible user attribute is clearly unfeasible due to huge amount of heterogenous user information. In this paper, we propose to select and combine a subset of user attributes with the goal to identify sources and targets for content dissemination in a social network. We develop a quantitative methodology based on the principal component analysis. Experiments on the YouTube and Flickr networks demonstrate that our solution outperforms existing solutions by 15%.