NDLI: Labels or attributes?: rethinking the neighbors for collective classification in sparsely-labeled networks

Content Provider	ACM Digital Library
Author	McDowell, Luke K. Aha, David W.
Abstract	Many classification 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 classification" based on neighbor attributes often has higher accuracy than "collective classification" based on neighbor labels. Moreover, we introduce an efficient method that enables discriminative classifiers 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.
Starting Page	847
Ending Page	852
Page Count	6
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505628
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Link-based classification Statistical relational learning Semi-supervised learning Social networks Collective inference
Content Type	Text
Resource Type	Article

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Labels or attributes?: rethinking the neighbors for collective classification in sparsely-labeled networks