Social influence is the behavioral change of a person because of the perceived relationship with other people, organizations and society in general. Social influence has been a widely accepted phenomenon in social networks for decades. Many applications have been built based around the implicit notation of social influence between people, such as marketing, advertisement and recommendations. With the exponential growth of online social network services such as Facebook and Twitter, social influence can for the first time be measured over a large population. In this tutorial, we survey the research on social influence analysis with a focus on the computational aspects. First, we introduce how to verify the existence of social influence in various social networks. Second, we present computational models for quantifying social influence. Third, we describe how social influence can help real applications. In particular, we will focus on opinion leader finding and influence maximization for viral marketing. Finally, we apply the selected algorithms of social influence analysis on different social network data, such as twitter, arnetminer data, weibo, and slashdot forum.