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.
|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)|
|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
|Name||Proceedings of Machine Learning Research|
|Conference||International Conference on Machine Learning 2017|
|Abbreviated title||ICML 2017|
|Period||6/08/17 → 11/08/18|
Zhao, H., Du, L., & Buntine, W. (2017). Leveraging node attributes for incomplete relational data. In D. Precup, & Y. W. Teh (Eds.), International Conference on Machine Learning, 6-11 August 2017, International Convention Centre, Sydney, Australia (pp. 4072-4081). (Proceedings of Machine Learning Research; Vol. 70). USA: Proceedings of Machine Learning Research (PMLR).