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Title: Leveraging Structure to Improve Classification Performance in Sparsely Labeled Networks

Abstract

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.

Authors:
;
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
926032
Report Number(s):
UCRL-TR-235752
TRN: US200807%%569
DOE Contract Number:  
W-7405-ENG-48
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; ALGORITHMS; CLASSIFICATION; PERFORMANCE

Citation Formats

Gallagher, B, and Eliassi-Rad, T. Leveraging Structure to Improve Classification Performance in Sparsely Labeled Networks. United States: N. p., 2007. Web. doi:10.2172/926032.
Gallagher, B, & Eliassi-Rad, T. Leveraging Structure to Improve Classification Performance in Sparsely Labeled Networks. United States. https://doi.org/10.2172/926032
Gallagher, B, and Eliassi-Rad, T. 2007. "Leveraging Structure to Improve Classification Performance in Sparsely Labeled Networks". United States. https://doi.org/10.2172/926032. https://www.osti.gov/servlets/purl/926032.
@article{osti_926032,
title = {Leveraging Structure to Improve Classification Performance in Sparsely Labeled Networks},
author = {Gallagher, B and Eliassi-Rad, T},
abstractNote = {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.},
doi = {10.2172/926032},
url = {https://www.osti.gov/biblio/926032}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Oct 22 00:00:00 EDT 2007},
month = {Mon Oct 22 00:00:00 EDT 2007}
}