PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction
Hisashi Kashima, Tsuyoshi Kato, Yoshihiro Yamanishi, Masashi Sugiyama and Koji Tsuda
In: 2009 SIAM International Conference on Data Mining, 30 Apr - 02 May 2009, Sparks, USA.

Abstract

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:4405
Deposited By:Koji Tsuda
Deposited On:13 March 2009