A Shrinkage Approach for Modeling Non-stationary Relational Autocorrelation

Recent research has shown that collective classification in relational data often exhibit significant performance gains over conventional approaches that classify instances individually. This is primarily due to the presence of autocorrelation in relational datasets, meaning that the class labels of related entities are correlated and inferences about one instance can be used to improve inferences about linked instances. Statistical relational learning techniques exploit relational autocorrelation by modeling global autocorrelation dependencies under the assumption that the level of autocorrelation is stationary throughout the dataset. To date, there has been no work examining the appropriateness of this stationarity assumption. In this paper, we examine two real-world datasets and show that there is significant variance in the autocorrelation dependencies throughout the relational data graphs. We develop a shrinkage technique for modeling this non-stationary autocorrelation and show that it achieves significant accuracy gains over competing techniques that model either local or global autocorrelation dependencies in isolation.