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Please use this identifier to cite or link to this item: http://hdl.handle.net/2451/27814

Title: Classification in Networked Data: A Toolkit and a Univariate Case Study
Authors: Mcskassy, Sofus
Provost, Foster
Keywords: relational learning
network learning
collective inference
collective classification
networked data
probabilistic relational models
network analysis
network data
Issue Date: May-2007
Publisher: Journal of Machine Learning Research
Citation: Journal of Machine Learning Research 8(May):935--983, 200
Series/Report no.: CeDER-PP-2007-07
Abstract: This paper1 is about classifying entities that are interlinked with entities for which the class is known. After surveying prior work, we present NetKit, a modular toolkit for classification in networked data, and a case-study of its application to networked data used in prior machine learning research. NetKit is based on a node-centric framework in which classifiers comprise a local classifier, a relational classifier, and a collective inference procedure. Various existing node-centric relational learning algorithms can be instantiated with appropriate choices for these components, and new combinations of components realize new algorithms. The case study focuses on univariate network classification, for which the only information used is the structure of class linkage in the network (i.e., only links and some class labels). To our knowledge, no work previously has evaluated systematically the power of class-linkage alone for classification in machine learning benchmark data sets. The results demonstrate that very simple network-classification models perform quite well—well enough that they should be used regularly as baseline classifiers for studies of learning with networked data. The simplest method (which performs remarkably well) highlights the close correspondence between several existing methods introduced for different purposes—that is, Gaussian-field classifiers, Hopfield networks, and relational-neighbor classifiers. The case study also shows that there are two sets of techniques that are preferable in different situations, namely when few versus many labels are known initially. We also demonstrate that link selection plays an important role similar to traditional feature selection
URI: http://hdl.handle.net/2451/27814
Appears in Collections:CeDER Published Papers

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