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Scalable Learning of Collective Behavior
June 2012 (vol. 24 no. 6)
pp. 1080-1091
ASCII Text
x 
 
Lei Tang, Xufei Wang, Huan Liu, "Scalable Learning of Collective Behavior," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 6, pp. 1080-1091, June, 2012.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2011.38,
author = {Lei Tang and Xufei Wang and Huan Liu},
title = {Scalable Learning of Collective Behavior},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {24},
number = {6},
issn = {1041-4347},
year = {2012},
pages = {1080-1091},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.38},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Scalable Learning of Collective Behavior
IS - 6
SN - 1041-4347
SP1080
EP1091
EPD - 1080-1091
A1 - Lei Tang,
A1 - Xufei Wang,
A1 - Huan Liu,
PY - 2012
KW - Classification with network data
KW - collective behavior
KW - community detection
KW - social dimensions.
VL - 24
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Lei Tang, Yahoo! Labs, Santa Clara
Xufei Wang, Arizona State University, Tempe
Huan Liu, Arizona State University, Tempe
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.

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Index Terms:
Classification with network data, collective behavior, community detection, social dimensions.
Citation:
Lei Tang, Xufei Wang, Huan Liu, "Scalable Learning of Collective Behavior," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 6, pp. 1080-1091, June 2012, doi:10.1109/TKDE.2011.38
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