Effective label acquisition for collective classification

TitleEffective label acquisition for collective classification
Publication TypeConference Papers
Year of Publication2008
AuthorsBilgic M, Getoor L
Conference NameProceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Date Published2008///
PublisherACM
Conference LocationNew York, NY, USA
ISBN Number978-1-60558-193-4
Keywordsactive inference, collective classification, label acquisition
Abstract

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.

URLhttp://doi.acm.org/10.1145/1401890.1401901
DOI10.1145/1401890.1401901