Detecting and Evolving Microblog Community Based on Structure and Gravity Cohesion

Abstract

Microblogs are open and real-time online social network platforms used by people to make posts about their moods, experiences, and interests. It will be very significant to gather microblog users who have similar interests and hobbies into the same community. In this paper, we propose novel approaches for detecting and evolving dynamic microblog communities. First, inspired by the universal gravitation law, we redefine the gravitation relationships among microblog users. Based on the structure of the microblog social network, we define the basic nodes and their gravity tendency and propose the microblog community detection algorithm. Second, we determine the community changes in the microblog social networks at times $t$ and $t+1$ and propose a microblog community evolution algorithm. Third, we define the mutual transformation probability between communities at times $t$ and $t+1$ and propose the microblog community evolution behavior algorithm. The experiment includes a comparison and evaluation of the microblog community detection, evolution, and behavior extraction algorithms and the optimal ranges of the parameters involved in these algorithms. The experimental results indicate that our proposed algorithms have good performance compared to other benchmarking methods.