Detailed Investigation: Performance of Influence Analysis Towards Big Social Data

Abstract

Large ranges of frequent information from online social network applications and wearable technology have seen an exponential growth in the number of users and activities recently. The social data offers well-heeled information and infinite potential for us to understand and analyze the complex inbuilt mechanism which governs the development of the new technology. Dissemination of data which represents the change in an individual's thoughts, attitudes and behaviors resulting from interaction with others, is one of the primary processes in social world. Therefore, influence analysis occupies a very important place in social related data analysis. We study the influence analysis under the scenario of big social data. First, we examine the uncertainty of influence relationship with the social network and a framework is introduced. To transform the uncertain networks into deterministic weight network a practical framework is proposed to explore the real changes of a social network data. Our penetrating framework will minimize the feasible difference between the experiential topology and the actual network through several representative communities.