Autocorrelation and Relational Learning: Challenges and Opportunities
Jennifer Neville, Özgür Şimşek, and David Jensen
In Proceedings of ICML-04 Workshop on Statistical Relational Learning, 2004.
Autocorrelation, a common characteristic of many datasets, refers to correlation between values of the same variable on related objects. It violates the critical assumption of instance independence that underlies most conventional models. In this paper, we provide an overview of research on autocorrelation in a number of fields with an emphasis on implications for relational learning, and outline a number of challenges and opportunities for model learning and inference.