Calculating influence based on the fusion of interest similarity and informationdissemination ability

With the popularization, in-depth development and application of Internet, microblogs have become a mainstream social network platform. Several studies on networks conducted researches, user influence evaluation is an important research hotspot. Most existing calculate by improving PageRank achieved certain results. However, these ignored fusion users' interest theme similarity information dissemination ability, analysis interaction behaviors among users not comprehensive. To address issues, we propose new microblog algorithm called based ability (MUI-ISIDA), which fully integrates user's ability. We construct model then allocate followers' contributions to bloggers reasonably. Considering quality microblogs, numbers forwarding, commenting, effective users, coefficient assimilation effect are designed. On this basis, was constructed. verified effectiveness proposed using real dataset. According experimental results, our higher accuracy in ranking than other state-of-the-art algorithms.