Collective classification with relational dependency networks

TL;DR: This paper presents relational dependency networks (RDNs), a collective classification model that offers simple parameter estimation and efficient structure learning and shows that collective classification improves performance.

Abstract: Collective classification models exploit the dependencies in a network of objects to improve predictions. For example, in a network of web pages, the topic of a page may depend on the topics of hyperlinked pages. A relational model capable of expressing and reasoning with such dependencies should achieve superior performance to relational models that ignore such dependencies. In this paper, we present relational dependency networks (RDNs), extending recent work in dependency networks to a relational setting. RDNs are a collective classification model that offers simple parameter estimation and efficient structure learning. On two real-world data sets, we compare RDNs to ordinary classification with relational probability trees and show that collective classification improves performance.