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.