Mining multiplex power-law distributions and retweeting patterns on twitter

Information exchange among people via social network service has produced a mass of communication data, which have been widely used in research on user interaction and information propagation on virtual social networks. The focus of this paper is to investigate the multiplex power-law distributions and retweeting patterns on Twitter platform. To achieve this goal, we analyze the multiplex power-law distributions from relationship network based on unidirectional and bidirectional follow connections and interaction network based on user and tweet entities. Further, we explain the observed features on each network. Additionally, we also explore the emergent pattern of tweet retweeting path and analyze their generative mechanisms. The observed results show that mining Twitter data from various angles could obtain more interesting discoveries in social networks.