Attribute Relationship Solving Method Based on Nodes and Communities in Opportunistic Social Networks

Abstract

The penetration of the 5G Internet and big data communication into human society brings about the survival basis of the social opportunistic networks. Using mobile terminal devices for communication makes the communication of nodes in the social opportunistic network intermittent, because nodes may be in motion all the time. In social opportunistic networks, data communication activities can be recorded and analyzed by evaluating communication activities of human beings or determining their interest points. However, the identification of nodes with the same or similar types of attributes among a large number of user nodes, has become a research problem in the field of social opportunistic networks. How to find an effective method to classify nodes according to their social characteristics and similarity degree becomes the key point of social opportunistic network data forwarding process. In this study, we proposed a method of community mining by decomposition of node and community relationship matrix with large social network data attributes. By using the regular type and iterative community features among community-rule-meet nodes, the method is proved to be converged and yield a minimum solution. Experimental results show that the proposed method exhibits strong application value.