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Scalable learning of collective behavior based on sparse social dimensions

Scalable learning of collective behavior based on sparse social dimensions Scalable Learning of Collective Behavior Based on Sparse Social Dimensions Lei Tang Computer Science & Engineering Arizona State University Tempe, AZ 85287, USA Huan Liu Computer Science & Engineering Arizona State University Tempe, AZ 85287, USA L.Tang@asu.edu ABSTRACT The study of collective behavior is to understand how individuals behave in a social network environment. Oceans of data generated by social media like Facebook, Twitter, Flickr and YouTube present opportunities and challenges to studying collective behavior in 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 is adopted to address the heterogeneity of connections presented in social media. However, the networks in social media are normally of colossal size, involving hundreds of thousands or even millions of actors. The scale of 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 socialdimension based approach can e ƒciently handle networks of millions of actors while demonstrating comparable http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Scalable learning of collective behavior based on sparse social dimensions

Association for Computing Machinery — Nov 2, 2009

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Datasource
Association for Computing Machinery
Copyright
The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc.
ISBN
978-1-60558-512-3
doi
10.1145/1645953.1646094
Publisher site
See Article on Publisher Site

Abstract

Scalable Learning of Collective Behavior Based on Sparse Social Dimensions Lei Tang Computer Science & Engineering Arizona State University Tempe, AZ 85287, USA Huan Liu Computer Science & Engineering Arizona State University Tempe, AZ 85287, USA L.Tang@asu.edu ABSTRACT The study of collective behavior is to understand how individuals behave in a social network environment. Oceans of data generated by social media like Facebook, Twitter, Flickr and YouTube present opportunities and challenges to studying collective behavior in 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 is adopted to address the heterogeneity of connections presented in social media. However, the networks in social media are normally of colossal size, involving hundreds of thousands or even millions of actors. The scale of 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 socialdimension based approach can e ƒciently handle networks of millions of actors while demonstrating comparable

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