Validation of Matching

Our matching problem setting is similar to the transductive setting for classification, from Vapnik [9], where there is a set of training examples with known inputs and class labels and a set of working examples with known inputs and unknown class labels, and the goal is to use the available training and working data to develop a classifier that classifies the working examples with a low error rate. For results on validation of network classifiers (rather than reconciliation algorithms) in transductive settings, refer to [10] and [11]. For theory and insight on why collective classification succeeds in general settings and validation methods for it, refer to [12]. For network reconciliation, we assume that we know some network data, consisting of some node data and the links, for both networks involved in the matching, and our goal is to use that network data to match nodes as accurately as possible between the networks. This paper presents a technique to compute probably approximately correct (PAC) bounds on the precision and recall of matching algorithms.