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

Kashima, H; Kato, T; Yamanishi , Y; Sugiyama, M; Tsuda, K

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

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

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https://epubs.siam.org/doi/10.1137/1.9781611972795.94
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Citation

Kashima, H., Kato, T., Yamanishi, Y., Sugiyama, M., & Tsuda, K. (2009). Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction. In H. Park, S. Parthasarathy, & H. Liu (Eds.), 2009 SIAM International Conference on Data Mining (SDM 2009) (pp. 1099-1110). Society for Industrial and Applied Mathematics: Philadelphia, PA, USA.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C4F1-9

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.