Abstract
With the rapid development of Internet technology and the widespread use of social networks in daily life, a large amount of information is propagated on the Web through various interactions among users. Researches on measuring users’ influence are becoming a hot spot, but the traditional methods are not suitable enough for identifying influential individuals in large-scale social networks. According to users’ time series behavior patterns of publishing information and their interested topics, we propose a TBRank model for mining individual influence of uses in different topics. Compared to other methods, our method can distinguish the difference of influence across different topics, and measure the influence of users more accurately. The experimental results on real dataset validate the effectiveness of our work.
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