This self-contained book describes social influence from a computational point of view, with a focus on recent and practical
applications, models, algorithms and open topics for future research. Researchers, scholars, postgraduates and developers
interested in research on social networking and the social influence related issues will find this book useful and motivating.
The latest research on social computing is presented along with and illustrations on how to understand and manipulate social
influence for knowledge discovery by applying various data mining techniques in real world scenarios. Experimental reports,
survey papers, models and algorithms with specific optimization problems are depicted. The main topics covered in this book
are: chrematistics of social networks, modeling of social influence propagation, popular research problems in social influence
analysis such as influence maximization, rumor blocking, rumor source detection, and multiple social influence competing.