Leveraging Node Attributes for Incomplete Relational Data

TL;DR

This paper introduces a Bayesian probabilistic model that uses node attributes to improve community detection and link prediction in incomplete relational networks, achieving state-of-the-art results.

Contribution

It presents a novel Bayesian approach that effectively incorporates binary node attributes into relational models for better performance with incomplete data.

Findings

Achieves state-of-the-art link prediction accuracy.

Works efficiently with sparse and incomplete relational data.

Flexible for directed and undirected networks.

Abstract

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.