Autocorrelation and Relational Learning:
Challenges and Opportunities

J. Neville, Ö. Şimşek and D. Jensen (2004). "Autocorrelation and relational learning: Challenges and opportunities." Proceedings of the Workshop on Statistical Relational Learning, 21st International Conference on Machine Learning.

Abstract

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.

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