With the development of Web technology, the Micro-Blog has become one of the most popular social platforms, and calculating and ranking the influences of the users on Micro-Blogs has been issuing an important research problem. Through improving the traditional the PageRank model, this paper presents a called PR4MB (PageRank for Micro-Blog) algorithm, which can obviously improve mining precisions for evaluating user influences on a Micro-Blog. While considering user link relations like the PageRank method, the PR4MB algorithm also takes attention to the activity, quality and credibility of a user on a Micro-Blog, so it constructs a dynamic mining model for user influences on a Micro-Blog by evaluation user online behaviors. The experimental results show that PR4MB algorithm, in comparing with the traditional PageRank algorithm, can more truly reflects the actual influences of different users on a Micro-Blog.
Mao, Guo-Jun and Zhang, Jie, "A PAGERANK-BASED MINING ALGORITHM FOR USER INFLUENCES ON MICRO-BLOGS" (2016). PACIS 2016 Proceedings. 226.