Collective Behavior of social Networking Sites
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2014, Vol 16, Issue 2
Abstract
Now a days a huge data is generated by social media like Facebook, Twitter, Flickr, and YouTube This big data present opportunities and challenges to study collective behavior of data. In this work, we predict collective behavior in social media. 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
Authors and Affiliations
Ashwini Vispute, Prerna Jadhav, Prof. P. V Kharat Kharat
Keywords
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- EP ID EP147364
- DOI 10.9790/0661-16227579
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