Abstract
In this paper, a diversity-dependent influence measure, considering social diversity and transition probability, is proposed for detecting the influencers by evaluating the influence of users across the social networks. Two models are then proposed to evaluate this measure. Comparative analyses on synthetic social networks and a real Twitter data suggest that the social diversities of the influenced people may play an important role in the identification of various influence levels of influencers. Comparative analysis between our proposed methods shows that the weighted spread strategy performs best. It implies that the pattern of the influence propagation would be beneficial to discover influencers. Our proposed scheme is therefore practical and feasible to be deployed in the real world.
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Huang, PY., Liu, HY., Lin, CT., Cheng, PJ. (2013). A Diversity-Dependent Measure for Discovering Influencers in Social Networks. In: Banchs, R.E., Silvestri, F., Liu, TY., Zhang, M., Gao, S., Lang, J. (eds) Information Retrieval Technology. AIRS 2013. Lecture Notes in Computer Science, vol 8281. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45068-6_32
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DOI: https://doi.org/10.1007/978-3-642-45068-6_32
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