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Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84004

Kashima,  H
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84265

Tsuda,  K
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Kashima, H., Kato T, Yamanishi Y, Sugiyama, M., & Tsuda, K. (2009). Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction. Proceedings of the 2009 SIAM International Conference on Data Mining (SDM 2009), 1099-1110.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-C4F1-9
Zusammenfassung
We propose Link Propagation as a new semi-supervised learning method for link prediction problems, where the task is to predict unknown parts of the network structure by using auxiliary information such as node similarities. Since the proposed method can fill in missing parts of tensors, it is applicable to multi-relational domains, allowing us to handle multiple types of links simultaneously. We also give a novel efficient algorithm for Link Propagation based on an accelerated conjugate gradient method.