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 social-dimension based approach can efficiently handle networks of millions of actors while demonstrating comparable prediction performance as other non-scalable methods.
| Original language | English (US) |
|---|---|
| Title of host publication | International Conference on Information and Knowledge Management, Proceedings |
| Pages | 1107-1116 |
| Number of pages | 10 |
| DOIs | |
| State | Published - 2009 |
| Event | ACM 18th International Conference on Information and Knowledge Management, CIKM 2009 - Hong Kong, China Duration: Nov 2 2009 → Nov 6 2009 |
Other
| Other | ACM 18th International Conference on Information and Knowledge Management, CIKM 2009 |
|---|---|
| Country | China |
| City | Hong Kong |
| Period | 11/2/09 → 11/6/09 |
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Keywords
- Behavior prediction
- Edge-centric clustering
- Relational learning
- Social dimensions
- Social media
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Decision Sciences(all)
Cite this
Scalable learning of collective behavior based on sparse social dimensions. / Tang, Lei; Liu, Huan.
International Conference on Information and Knowledge Management, Proceedings. 2009. p. 1107-1116.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Scalable learning of collective behavior based on sparse social dimensions
AU - Tang, Lei
AU - Liu, Huan
PY - 2009
Y1 - 2009
N2 - 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 social-dimension based approach can efficiently handle networks of millions of actors while demonstrating comparable prediction performance as other non-scalable methods.
AB - 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 social-dimension based approach can efficiently handle networks of millions of actors while demonstrating comparable prediction performance as other non-scalable methods.
KW - Behavior prediction
KW - Edge-centric clustering
KW - Relational learning
KW - Social dimensions
KW - Social media
UR - http://www.scopus.com/inward/record.url?scp=74549120273&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=74549120273&partnerID=8YFLogxK
U2 - 10.1145/1645953.1646094
DO - 10.1145/1645953.1646094
M3 - Conference contribution
AN - SCOPUS:74549120273
SN - 9781605585123
SP - 1107
EP - 1116
BT - International Conference on Information and Knowledge Management, Proceedings
ER -