Large scale social data from online social networks, instant messaging applications, and wearable devices have seen an exponential growth in a number of users and activities recently. The rapid proliferation of social data provides rich information and infinite possibilities for us to understand and analyze the complex inherent mechanism which governs the evolution of the new technology age. Influence, as a natural product of information diffusion (or propagation), which represent the change in an individual's thoughts, attitudes, and behaviors that results from interaction with others, is one of the fundamental processes taking place in social worlds. Therefore, influence analysis occupies a very prominent place in social related data analysis, theory, model, and algorithms.
This presentation is going to introducing the study of influence analysis under the scenario of big social data. The subtopic includes: (1) how to investigate the uncertainty of influence relationship among the social network; (2) how to deal with the dynamic of social data, (3) how to take the full advantage of divide-and-conquer strategy to reduces the computational overhead in influence analysis; (4) how to address the breadth as well as the depth in influence analysis and (5) how to face the challenge of large-scale data. Besides, several potential research direction and future works will also be covered at the end of the presentation.