Abstract
| Original language | English |
|---|---|
| Title of host publication | International Conference on Machine Learning, 6-11 August 2017, International Convention Centre, Sydney, Australia |
| Editors | Doina Precup, Yee Whye Teh |
| Place of Publication | USA |
| Publisher | Proceedings of Machine Learning Research (PMLR) |
| Pages | 4072-4081 |
| Number of pages | 10 |
| Publication status | Published - 2017 |
| Event | International Conference on Machine Learning 2017 - International Convention Centre , Sydney , Australia Duration: 6 Aug 2017 → 11 Aug 2018 Conference number: 34th https://icml.cc/Conferences/2017 https://2017.icml.cc/ |
Publication series
| Name | Proceedings of Machine Learning Research |
|---|---|
| Publisher | PMLR |
| Volume | 70 |
| ISSN (Print) | 1938-7228 |
Conference
| Conference | International Conference on Machine Learning 2017 |
|---|---|
| Abbreviated title | ICML 2017 |
| Country | Australia |
| City | Sydney |
| Period | 6/08/17 → 11/08/18 |
| Internet address |
Cite this
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Leveraging node attributes for incomplete relational data. / Zhao, He; Du, Lan; Buntine, Wray.
International Conference on Machine Learning, 6-11 August 2017, International Convention Centre, Sydney, Australia. ed. / Doina Precup; Yee Whye Teh. USA : Proceedings of Machine Learning Research (PMLR), 2017. p. 4072-4081 (Proceedings of Machine Learning Research; Vol. 70).Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
TY - GEN
T1 - Leveraging node attributes for incomplete relational data
AU - Zhao, He
AU - Du, Lan
AU - Buntine, Wray
PY - 2017
Y1 - 2017
N2 - Relational data are usually highly incomplete in practice, which inspires us to leverage side information to improve the performance of community detection and link prediction. This paper presents a Bayesian probabilistic approach that incorporates various kinds of node attributes encoded in binary form in relational models with Poisson likelihood. Our method works flexibly with both directed and undirected relational networks. The inference can be done by efficient Gibbs sampling which leverages sparsity of both networks and node attributes. Extensive experiments show that our models achieve the state-of-the-art link prediction results, especially with highly incomplete relational data.
AB - Relational data are usually highly incomplete in practice, which inspires us to leverage side information to improve the performance of community detection and link prediction. This paper presents a Bayesian probabilistic approach that incorporates various kinds of node attributes encoded in binary form in relational models with Poisson likelihood. Our method works flexibly with both directed and undirected relational networks. The inference can be done by efficient Gibbs sampling which leverages sparsity of both networks and node attributes. Extensive experiments show that our models achieve the state-of-the-art link prediction results, especially with highly incomplete relational data.
M3 - Conference Paper
T3 - Proceedings of Machine Learning Research
SP - 4072
EP - 4081
BT - International Conference on Machine Learning, 6-11 August 2017, International Convention Centre, Sydney, Australia
A2 - Precup, Doina
A2 - Teh, Yee Whye
PB - Proceedings of Machine Learning Research (PMLR)
CY - USA
ER -