Community discovery algorithm under big data: taking microblog as an example

Abstract

Microblog has become a popular social media because of its short text and timely release, and its impact on society has gradually increased. In order to study the behaviour of microblog users, this paper introduced two algorithms for microblog network community division, which were community discovery algorithm based on density peak clustering and community discovery algorithm based on similarity. Then the two algorithms were simulated using MATLAB software. The data used in the experiment included the artificial network generated by LFR tool and the following information data of different users collected by taking the API interface of the microblog of a student from Computer School of Jining Normal University as the starting point by crawlers. The results demonstrated that the normalised mutual information (NMI) and the density of the community structure obtained by the two algorithms decreased, and the conductivity increased with the expansion of the scale of microblog network, and the community structure obtained by the similarity-based algorithm had higher NMI and density and lower conductivity under the same scale of micrblog network. In conclusion, the similarity-based algorithm can divide microblog network better.