### 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.

Original language | English |
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Title of host publication | Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics 133 |

Pages | 1093-1104 |

Number of pages | 12 |

Publication status | Published - Dec 1 2009 |

Event | 9th SIAM International Conference on Data Mining 2009, SDM 2009 - Sparks, NV, United States Duration: Apr 30 2009 → May 2 2009 |

### Publication series

Name | Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics |
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Volume | 3 |

### Other

Other | 9th SIAM International Conference on Data Mining 2009, SDM 2009 |
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Country | United States |

City | Sparks, NV |

Period | 4/30/09 → 5/2/09 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Computational Theory and Mathematics
- Software
- Applied Mathematics

### Cite this

*Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics 133*(pp. 1093-1104). (Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics; Vol. 3).

**Link propagation : A fast semi-supervised learning algorithm for link prediction.** / Kashima, Hisashi; Kato, Tsuyoshi; Yamanishi, Yoshihiro; Sugiyama, Masashi; Tsuda, Koji.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics 133.*Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics, vol. 3, pp. 1093-1104, 9th SIAM International Conference on Data Mining 2009, SDM 2009, Sparks, NV, United States, 4/30/09.

}

TY - GEN

T1 - Link propagation

T2 - A fast semi-supervised learning algorithm for link prediction

AU - Kashima, Hisashi

AU - Kato, Tsuyoshi

AU - Yamanishi, Yoshihiro

AU - Sugiyama, Masashi

AU - Tsuda, Koji

PY - 2009/12/1

Y1 - 2009/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=73449109177&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=73449109177&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:73449109177

SN - 9781615671090

T3 - Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics

SP - 1093

EP - 1104

BT - Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics 133

ER -