Leveraging Structure to Improve Classification Performance in Sparsely Labeled Networks

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We address the problem of classification in a partially labeled network (a.k.a. within-network classification), with an emphasis on tasks in which we have very few labeled instances to start with. Recent work has demonstrated the utility of collective classification (i.e., simultaneous inferences over class labels of related instances) in this general problem setting. However, the performance of collective classification algorithms can be adversely affected by the sparseness of labels in real-world networks. We show that on several real-world data sets, collective classification appears to offer little advantage in general and hurts performance in the worst cases. In this paper, we ... continued below

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Gallagher, B & Eliassi-Rad, T October 22, 2007.

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Description

We address the problem of classification in a partially labeled network (a.k.a. within-network classification), with an emphasis on tasks in which we have very few labeled instances to start with. Recent work has demonstrated the utility of collective classification (i.e., simultaneous inferences over class labels of related instances) in this general problem setting. However, the performance of collective classification algorithms can be adversely affected by the sparseness of labels in real-world networks. We show that on several real-world data sets, collective classification appears to offer little advantage in general and hurts performance in the worst cases. In this paper, we explore a complimentary approach to within-network classification that takes advantage of network structure. Our approach is motivated by the observation that real-world networks often provide a great deal more structural information than attribute information (e.g., class labels). Through experiments on supervised and semi-supervised classifiers of network data, we demonstrate that a small number of structural features can lead to consistent and sometimes dramatic improvements in classification performance. We also examine the relative utility of individual structural features and show that, in many cases, it is a combination of both local and global network structure that is most informative.

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PDF-file: 16 pages; size: 0.3 Mbytes

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  • Report No.: UCRL-TR-235752
  • Grant Number: W-7405-ENG-48
  • DOI: 10.2172/926032 | External Link
  • Office of Scientific & Technical Information Report Number: 926032
  • Archival Resource Key: ark:/67531/metadc901790

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  • October 22, 2007

Added to The UNT Digital Library

  • Sept. 27, 2016, 1:39 a.m.

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  • Dec. 7, 2016, 4:31 p.m.

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Gallagher, B & Eliassi-Rad, T. Leveraging Structure to Improve Classification Performance in Sparsely Labeled Networks, report, October 22, 2007; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc901790/: accessed October 23, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.