Validation of Network Classifiers

Abstract

This paper develops PAC (probably approximately correct) error bounds for network classifiers in the transductive setting, where the network node inputs and links are all known, the training nodes class labels are known, and the goal is to classify a working set of nodes that have unknown class labels. The bounds are valid for any model of network generation. They require working nodes to be selected independently, but not uniformly at random. For example, they allow different regions of the network to have different densities of unlabeled nodes.