Abstract
User influence ranking is an important subject of social network research, for public opinion control, important dissemination and so on is a great significance. The traditional PageRank algorithm only considers the relationship between users, this paper combines the Page Rank algorithm with the user behavior, integrates the follower quality, comment rate, forwarding rate and whether the authentication behavior and other user behavior factors, proposed an improved algorithm URank. Experiments based on the SIR propagation model show that the URank algorithm is superior to the PageRank algorithm in calculating the accuracy of the influence index for large-scale data sets.
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