Title: A quantitative methodology based on component analysis to identify key users in social networks

Authors: Claudia Canali; Riccardo Lancellotti

Addresses: Department of Information Engineering, University of Modena and Reggio Emilia, Via vignolese 905, I-41125 Modena, Italy. ' Department of Information Engineering, University of Modena and Reggio Emilia, Via vignolese 905, I-41125 Modena, Italy

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%.

Keywords: social network analysis; key user identification; content dissemination; principal component analysis; PCA; key users; social networks; user attributes; YouTube; Flickr.

DOI: 10.1504/IJSNM.2012.045104

International Journal of Social Network Mining, 2012 Vol.1 No.1, pp.27 - 50

Received: 08 Mar 2011
Accepted: 06 Jul 2011
Published online: 21 Aug 2014 *

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