PageRank with Priors: An Influence Propagation Perspective

Abstract

Recent years have witnessed increased interests in measuring authority and modelling influence in social networks. For a long time, PageRank has been widely used for authority computation and has also been adopted as a solid baseline for evaluating social influence related applications. However, the connection between authority measurement and influence modelling is not clearly established. To this end, in this paper, we provide a focused study on understanding of PageRank as well as the relationship between PageRank and social influence analysis. Along this line, we first propose a linear social influence model and reveal that this model is essentially PageRank with prior. Also, we show that the authority computation by PageRank can be enhanced with more generalized priors. Moreover, to deal with the computational challenge of PageRank with general priors, we provide an upper bound for top authoritative nodes identification. Finally, the experimental results on the scientific collaboration network validate the effectiveness of the proposed social influence model.