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Classification in Networked Data: A toolkit and a univariate case study (2006)
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BibTeX
@MISC{Macskassy06classificationin,
author = {Sofus A. Macskassy and Foster Provost},
title = {Classification in Networked Data: A toolkit and a univariate case study},
year = {2006}
}
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Abstract
This paper 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—i.e., 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.
Keyphrases
networked data univariate case study case study node-centric framework modular toolkit appropriate choice important role class label relational classifier relational-neighbor classifier new combination prior work different situation univariate network classification class linkage new algorithm link selection present netkit machine learning benchmark data set class-linkage alone simple network-classification model node-centric relational learning algorithm baseline classifier local classifier collective inference procedure traditional feature selection gaussian-field classifier prior machine close correspondence many label hopfield network different purpose
