A new evaluation algorithm for the influence of user in social network

Online social networks have gradually permeated into every aspect of people's life. As a research hotspot in social network, user influence is of theoretical and practical significant for information transmission, optimization and integration. A prominent application is a viral marketing campaign which aims to use a small number of targeted influence users to initiate cascades of influence that create a global increase in product adoption. In this paper, we analyze mainly evaluation methods of user influence based on IDM evaluation model, PageRank evaluation model, use behavior model and some other popular influence evaluation models in currently social network. And then, we extract the core idea of these models to build our influence evaluation model from two aspects, relationship and activity. Finally, the proposed approach was validated on real world datasets, and the result of experiments shows that our method is both effective and stable.