Empirical Inference
Conference Paper
2009
Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction
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 |
| Bibtex Type: | Conference Paper (inproceedings) |
| Address: | Society for Industrial and Applied Mathematics |
| Event Name: | SDM 2009 |
| Event Place: | Sparks, NV, USA |
| Electronic Archiving: | grant_archive |
| Institution: | Society for Industrial and Applied Mathematics |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@inproceedings{5654,
title = {Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction},
booktitle = {Proceedings of the 2009 SIAM International Conference on Data Mining},
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.},
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},
slug = {5654},
author = {Kashima, H. and Kato, T. and Yamanishi, Y. and Sugiyama, M. and Tsuda, K.},
month_numeric = {5}
}