Social Dimension Extraction for Scalable Learning of Collective Behaviour
Source: International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE)
Online social networks play an important role in everyday life for many people. Social media has reshaped the way in which people interact with each other. The rapid development of participatory web and social networking sites like YouTube, Twitter, and Face book also brings about many data mining opportunities and novel challenges. In particular, given information about some individuals, how can the people infer the behavior of unobserved individuals in the same network? A social-dimension-based approach has been shown effective in addressing the heterogeneity of connections presented in social media. However, the networks in social media are normally of colossal size, involving hundreds of thousands of actors. The scale of these networks entails scalable learning of models for collective behavior prediction.