DOI 10.1007/s10618-012-0278-6 Network regression with predictive clustering trees

Abstract Network data describe entities represented by nodes, which may be con-nected with (related to) each other by edges. Many network datasets are characterized by a form of autocorrelation, where the value of a variable at a given node depends on the values of variables at the nodes it is connected with. This phenomenon is a direct violation of the assumption that data are independently and identically distrib-uted. At the same time, it offers an unique opportunity to improve the performance of predictive models on network data, as inferences about one entity can be used to improve inferences about related entities. Regression inference in network data is a challenging task. While many approaches for network classification exist, there are very few approaches for network regression. In this paper, we propose a dat