This study of collective behavior
is to understand how individuals behave in a social networking environment.
Oceans of data generated by social media like Facebook, Twitter, Flickr, and
YouTube present opportunities and challenges to study collective behavior on a
large scale. In this work, we aim to learn to 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 nonscalable methods.
Keywords:- IEEE Project 2012, Data Mining Titles,
Wireless Communication Titles, Networking Tiles.
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