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We consider the problem of obtaining unbiased estimates of group propertiesin social networks when using a classifier for node labels. Inference for thisproblem is complicated by two factors: the network is not known and must becrawled, and even high-performance classifiers provide biased estimates ofgroup proportions. We propose and evaluate AdjustedWalk for addressing thisproblem. This is a three step procedure which entails: 1) walking the graphstarting from an arbitrary node; 2) learning a classifier on the nodes in thewalk; and 3) applying a post-hoc adjustment to classification labels. The walkstep provides the information necessary to make inferences over the nodes andedges, while the adjustment step corrects for classifier bias in estimatinggroup proportions. This process provides de-biased estimates at the cost ofadditional variance. We evaluate AdjustedWalk on four tasks: the proportion ofnodes belonging to a minority group, the proportion of the minority group amonghigh degree nodes, the proportion of within-group edges, and Coleman'shomophily index. Simulated and empirical graphs show that this procedureperforms well compared to optimal baselines in a variety of circumstances,while indicating that variance increases can be large for low-recallclassifiers.

Estimating group properties in online social networks with a classifier