Leveraging Node Attributes for Incomplete Relational Data
This work addresses the challenge of community detection and link prediction in incomplete relational networks, which is an incremental improvement for researchers and practitioners in network analysis.
The paper tackles the problem of incomplete relational data by introducing a Bayesian probabilistic approach that incorporates binary node attributes, achieving state-of-the-art link prediction results, particularly with highly incomplete data.
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.