Applying an Influence Measurement Framework to Large Social Network
Khaled Almgren, Jeongkyu Lee
Journal of Networking Technology, Vol. 7, No. 1, 2016.
DOI: 
Link: http://www.dline.info/jnt/fulltext/v7n1/v7n1_2.pdf
Abstract:
Predicting influential users is one of the important research problems on social network analysis. It helps
to understand many complicated phenomena including information dissemination. It can be employed in many real world
applications such as viral marketing. Influential users can influence social network users using their attributes, strategic
locations or expertises. In this paper, we tackle this problem by proposing a novel hybrid framework that is used to
predict influential users on social network. In this framework, we integrate usersâ€™ attributes and their strategic location
to measure influence. We apply several centrality analysis algorithms to find userâ€™s strategic locations, while we adapt a
real world attribute measure that is used by Flickr based on usersâ€™ attributes. We employ our framework to a large
dataset crawled from real world social network, i.e., Digg. We evaluate the proposed framework in term of correlation. We
further show that the proposed framework outperforms other measurements