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Probabilistic Diffusion of Social Influence with Incentives
PrePrint
ISSN: 1939-1374
With explosive growth of social media, social computing becomes a new IT feature. A core functionality of social computing is social network analysis, which studies dynamics of social connectivity among people, including how people influence one another and how fast information diffuses in a social network and what factors stimulate influence diffusion. One of the models for information diffusion is the heat diffusion model. Although it is simple in capturing the basic principle of social influence, there are several limitations. First, the uniform heat diffusion is no longer hold in social networks. Second, high degree nodes are most influential in all contexts is not realistic. In this paper we propose a probabilistic approach of social influence diffusion model with incentives. Our approach has three features. First we define an influence diffusion probability for each node instead of uniform probability. Second, we categorize nodes into two classes: active and inactive. Active nodes have chances to influence inactive nodes but not vice versa. Third, we utilize a system defined diffusion threshold to control how influence is propagated. We study how incentives can be utilized to boost the influence diffusion. Our experiments show the reward-powered model is more effective in influence diffusion.
Citation:
Myungcheol Doo, Ling Liu, "Probabilistic Diffusion of Social Influence with Incentives," IEEE Transactions on Services Computing, 24 March 2014. IEEE computer Society Digital Library. IEEE Computer Society, <http://doi.ieeecomputersociety.org/10.1109/TSC.2014.2310216>
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