Predicting user influence based on improved linear threshold model in social networks

With the scale of social networks growing rapidly, the amount of user participating in it increases at astonishing speed. Predicting user influence in social networks is an interesting and useful research direction. There are lots of works on this area, mainly covering the following aspects: Maximize Influence, Influence Diffusion, Predicting Influential Users and Predicting social influence. The common issue of above aspects is that they can't calculate the precise user influence. In this work, we propose a novel method to give each user influence a precise value in social networks. The proposed method is based on Improved Linear Threshold Model (ILTM). To build ILTM, we take a series of measures to overcome the deficiencies brought by Linear Threshold Model, such as assigning random threshold to node, cold start. So we adopt user similarity instead of random threshold and utilize cluster algorithm and link analysis algorithm to surmount cold start. © 2014 by Binary Information Press