MPURank: A Social Hotspot Tracking Scheme Based on Tripartite Graph and Multimessages Iterative Driven

Abstract

In social networks, the identification of key elements in a topic propagation network, including key messages, paths, and users, is important in network public opinion mining and control. In view of the three key elements, this paper proposes a social hotspot tracking scheme termed MPURank, which is based on tripartite graphing and a multimessage-driven iterative method. First, the topic is regarded as a network of simultaneous multimessage dissemination, and retweeting topology graphing is used to determine the propagation path; then, a topic tripartite graph is established to elucidate the interrelationship among the nodes of messages, paths, and users. Second, an iterative scoring algorithm based on hyperlink-induced topic search is proposed to rank the key elements based on the tripartite graph. Using the initial score vectors of different elements and weight matrices, the algorithm obtains the final score sequences of the elements. This algorithm circumvents the influence of the multimessage network and propagation path on the evolution of public opinion, overcoming the issues of node attributes and topics, and the influence of multimessage and multipath complexity in source tracing, thus identifying the key elements. Finally, we conducted an experiment based on real-world data, which verifies the utility of the algorithm.