Using graphs for social data mining

Social data are accumulated rapidly with the prosperous growth of social network services. The user generated content, such as relations, shares, blogs, comments, are reflecting people's real life experiences on many kinds of social network platforms, including Facebook, LinkedIn, Twitter, Tumblr, Pinterest, Foursquare, and so on. Such social data grant researchers the new opportunities to understand a whole spectrum of new phenomena related to users. For example, data scientists could discover hidden and promising communities, identify influential people in the vast social media population, infer dynamic or static relations, etc. The capability to make reasons out of social data becomes critical for many aspects in data mining tasks, including decision making, business intelligence, computational advertising, recommendation system, as well as data mining methodologies, e.g., clustering, classification, ranking, with social flavor.