document: Effective label acquisition for collective classification Information diffusion , viral marketing , and collective classification all attempt to model and exploit the relationships in a network to make inferences about the labels of nodes . A variety of techniques have been introduced and methods that combine attribute information and neighboring label information have been shown to be effective for collective labeling of the nodes in a network . However , in part because of the correlation between node labels that the techniques exploit , it is easy to find cases in which , once a misclassification is made , incorrect information propagates throughout the network . This problem can be mitigated if the system is allowed to judiciously acquire the labels for a small number of nodes . Unfortunately , under relatively general assumptions , determining the optimal set of labels to acquire is intractable . Here we propose an acquisition method that learns the cases when a given collective classification algorithm makes mistakes , and suggests acquisitions to correct those mistakes . We empirically show on both real and synthetic datasets that this method significantly outperforms a greedy approximate inference approach , a viral marketing approach , and approaches based on network structural measures such as node degree and network clustering . In addition to significantly improving accuracy with just a small amount of labeled data , our method is tractable on large networks .
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