Behavior Prediction Via Social Dimensions Extraction
M.Nagendramma1, K.Subba Reddy2

1M.Nagendramma, Department of CSE, Prakasam Engineering College, Kandukur (Andhra Pradesh), India.
2Prof. K. Subbareddy, Department of IT, Prakasam Engineering College, Kandukur (Andhra Pradesh), India.
Manuscript received on 18 August 2012 | Revised Manuscript received on 25 August 2012 | Manuscript published on 30 August 2012 | PP: 28-32 | Volume-1 Issue-3, August 2012 | Retrieval Number: C0255061312/2012©BEIESP
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Abstract: 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 we 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. To address the scalability issue, we propose an edge-centric clustering scheme to extract sparse social dimensions. With sparse social dimensions, the proposed approach can efficiently handle networks of millions of actors while demonstrating a comparable prediction performance to other non-scalable methods.
Keywords: Classification with Network Data, Collective Behavior, Community Detection, Social Dimensions.

Scope of the Article: Classification