Collective Inference for Handling Autocorrelation in Network Regression

In predictive data mining tasks, we should account for autocorrelations of both the independent variables and the dependent variable, which we can observe in neighborhood of a target node and that same node. The prediction on a target node should be based on the value of the neighbours which might even be unavailable. To address this problem, the values of the neighbours should be inferred collectively. We present a novel computational solution to perform collective inferences in a network regression task. We define an iterative algorithm, in order to make regression inferences about predictions of multiple nodes simultaneously and feed back the more reliable predictions made by the previous models in the labeled network. Experiments investigate the effectiveness of the proposed algorithm in spatial networks