Transaction-based link strength prediction in a social network

Khosravi, Hassan, Bozorgkhan, Ali and Schulte, Oliver (2013). Transaction-based link strength prediction in a social network. In: Proceedings of the 2013 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013. 2013 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013, Singapore, (191-198). 16-19 April 2013. doi:10.1109/CIDM.2013.6597236


Author Khosravi, Hassan
Bozorgkhan, Ali
Schulte, Oliver
Title of paper Transaction-based link strength prediction in a social network
Conference name 2013 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
Conference location Singapore
Conference dates 16-19 April 2013
Proceedings title Proceedings of the 2013 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
Series Proceedings of the 2013 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2013
Sub-type Fully published paper
DOI 10.1109/CIDM.2013.6597236
ISBN 9781467358958
Start page 191
End page 198
Total pages 8
Abstract/Summary The revolution of social networks and methods of analyzing them have attracted interest in many research fields. Predicting whether a friendship holds in a social network between two individuals or not, link prediction, has been a heavily researched topic in the last decade. In this paper we investigate a related problem, link strength prediction: how to assign ratings or strengths to friendship links. A basic approach would be matrix factorization applied to only friendship ratings. However, the existence of extensive transactions among users may be used for better predictions. We propose a new type of multiple-matrix factorization model for incorporating a transaction matrix. We derive gradient descent update equations for learning latent factors that predict values in the target rating matrix. Multiple-matrix factorization can be seen as a data fusion technique, that combines evidence from different sources. In the social network application, the target matrix contains friendship ratings and the evidence matrices specify transaction intensities between users. To evaluate the model, we introduce data from Cloob, a popular Iranian social network as well as synthetic data.
Subjects 1702 Cognitive Sciences
1712 Software
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Conference Paper
Collection: Institute for Teaching and Learning Innovation Publications
 
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Created: Thu, 15 Sep 2016, 02:28:07 EST by Hassan Khosravi on behalf of Learning and Research Services (UQ Library)