A Correlation Clustering Approach to Link Classification in Signed Networks
Abstract
Motivated by social balance theory, we develop a theory of link classification in signed networks using the correlation clustering index as measure of label regularity. We derive learning bounds in terms of correlation clustering within three fundamental transductive learning settings: online, batch and active. Our main algorithmic contribution is in the active setting, where we introduce a new family of efficient link classifiers based on covering the input graph with small circuits. These are the first active algorithms for link classification with mistake bounds that hold for arbitrary signed networks.
Cite
Text
Cesa-Bianchi et al. "A Correlation Clustering Approach to Link Classification in Signed Networks." Proceedings of the 25th Annual Conference on Learning Theory, 2012.Markdown
[Cesa-Bianchi et al. "A Correlation Clustering Approach to Link Classification in Signed Networks." Proceedings of the 25th Annual Conference on Learning Theory, 2012.](https://mlanthology.org/colt/2012/cesabianchi2012colt-correlation/)BibTeX
@inproceedings{cesabianchi2012colt-correlation,
title = {{A Correlation Clustering Approach to Link Classification in Signed Networks}},
author = {Cesa-Bianchi, Nicoló and Gentile, Claudio and Vitale, Fabio and Zappella, Giovanni},
booktitle = {Proceedings of the 25th Annual Conference on Learning Theory},
year = {2012},
pages = {34.1-34.20},
volume = {23},
url = {https://mlanthology.org/colt/2012/cesabianchi2012colt-correlation/}
}