TitleLabels or attributes? Rethinking the neighbors for collective classification in sparsely-labeled networks
Publication TypeConference Paper
Year of Publication2013
AuthorsMcDowell, LK, Aha, DW
Conference NameInternational Conference on Information and Knowledge Management
PublisherACM Press
Conference LocationSan Francisco, CA
Keywordscollective classification, machine learning, sparsely labeled networks

Many classication tasks involve linked nodes, such as people connected by friendship links. For such networks, accuracy might be increased by including, for each node, the (a) labels or (b) attributes of neighboring nodes as model features. Recent work has focused on option (a), because early work showed it was more accurate and because option (b) fit poorly with discriminative classifiers. We show, however, that when the network is sparsely labeled, relational classication based on neighbor attributes often has higher accuracy than collective classification based on neighbor labels. Moreover, we introduce an ecient method that enables discriminative classiers to be used with neighbor attributes, yielding further accuracy gains. We show that these effects are consistent across a range of datasets, learning choices, and inference algorithms, and that using both neighbor attributes and labels often produces the best accuracy.

Refereed DesignationRefereed
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machine learning
collective classification
sparsely labeled networks