Fast Learning of Relational Dependency Networks

TL;DR

This paper introduces a fast method for learning relational dependency networks by transforming Bayesian networks, enabling scalable learning on large datasets with competitive accuracy.

Contribution

The paper presents a novel approach that transforms Bayesian networks into RDNs, significantly improving learning speed and scalability for large relational datasets.

Findings

Learning RDNs via BNs scales better to large datasets.

The method achieves comparable prediction accuracy to state-of-the-art methods.

Transforming BNs into RDNs is efficient and effective for big data.

Abstract

A Relational Dependency Network (RDN) is a directed graphical model widely used for multi-relational data. These networks allow cyclic dependencies, necessary to represent relational autocorrelations. We describe an approach for learning both the RDN's structure and its parameters, given an input relational database: First learn a Bayesian network (BN), then transform the Bayesian network to an RDN. Thus fast Bayes net learning can provide fast RDN learning. The BN-to-RDN transform comprises a simple, local adjustment of the Bayes net structure and a closed-form transform of the Bayes net parameters. This method can learn an RDN for a dataset with a million tuples in minutes. We empirically compare our approach to state-of-the art RDN learning methods that use functional gradient boosting, on five benchmark datasets. Learning RDNs via BNs scales much better to large datasets than learning RDNs with boosting, and provides competitive accuracy in predictions.