h1

ICML 2017 paper: Leveraging Node Attributes for Incomplete Relational Data

May 19, 2017

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 where entertaining, and some interesting results we didn’t get in the paper.  Its always enlightening doing comparative experiments.

Advertisements

One comment

  1. […] Topical insights into components of prior research and aspects of latent academia « ICML 2017 paper: Leveraging Node Attributes for Incomplete Relational Data […]



Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

%d bloggers like this: