Here is a paper with Ethan Zhao and Lan Du, both of Monash, we’ll present in Sydney.
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.
As usual, the reviews were entertaining, and some interesting results we didn’t get in the paper. Its always enlightening doing comparative experiments.
Bayesian Models 