Network data describe entities represented by nodes, which may be connected 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 distributed. At the same time, it offers
an unique opportunity to improve the performance of predictive models on network data, as …

Regression inference in network data is a challenging task in machine learning and data
mining. Network data describe entities represented by nodes, which may be connected with
(related to) each other by edges. Many network datasets are characterized by a form of
autocorrelation where the values of the response variable at a given node depend on the
values of the variables (predictor and response) at the nodes connected to the given node.
This phenomenon is a direct violation of the assumption of independent (iid) observations …