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

2009

Conference Paper

ei


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.

Author(s): Kashima, H. and Kato, T. and Yamanishi, Y. and Sugiyama, M. and Tsuda, K.
Book Title: Proceedings of the 2009 SIAM International Conference on Data Mining
Pages: 1099-1110
Year: 2009
Month: May
Day: 0
Editors: Park, H. , S. Parthasarathy, H. Liu
Publisher: Philadelphia, PA, USA

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: SDM 2009
Event Place: Sparks, NV, USA

Address: Society for Industrial and Applied Mathematics
Institution: Society for Industrial and Applied Mathematics
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{5654,
  title = {Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction},
  author = {Kashima, H. and Kato, T. and Yamanishi, Y. and Sugiyama, M. and Tsuda, K.},
  booktitle = {Proceedings of the 2009 SIAM International Conference on Data Mining},
  pages = {1099-1110},
  editors = {Park, H. , S. Parthasarathy, H. Liu},
  publisher = {Philadelphia, PA, USA},
  organization = {Max-Planck-Gesellschaft},
  institution = {Society for Industrial and Applied Mathematics},
  school = {Biologische Kybernetik},
  address = {Society for Industrial and Applied Mathematics},
  month = may,
  year = {2009},
  month_numeric = {5}
}