Abstract

The increasing availability of participatory web and social media presents enormous opportunities to study human relations and collective behaviors. Many applications involving decision making want to obtain certain generalized properties about the population in a network, such as the proportion of actors given a category, instead of the category of individuals. While data mining and machine learning researchers have developed many methods for link-based classification or relational learning, most are optimized to classify individual nodes in a network. In order to accurately estimate the prevalence of one class in a network, some quantification method has to be used. In this work, two kinds of approaches are presented: quantification based on classification or quantification based on link analysis. Extensive experiments are conducted on several representative network data, with interesting findings reported concerning efficacy and robustness of different quantification methods, providing insights to further quantify the ebb and flow of online collective behaviors at macro-level.

Original languageEnglish (US)
Title of host publicationProceedings of the 8th Workshop on Mining and Learning with Graphs, MLG'10
Pages147-154
Number of pages8
DOIs
StatePublished - Sep 8 2010
Event8th Workshop on Mining and Learning with Graphs, MLG'10 - Washington, DC, United States
Duration: Jul 24 2010Jul 25 2010

Publication series

NameProceedings of the 8th Workshop on Mining and Learning with Graphs, MLG'10

Other

Other8th Workshop on Mining and Learning with Graphs, MLG'10
CountryUnited States
CityWashington, DC
Period7/24/107/25/10

Keywords

  • Classification-based quantification
  • Link-based quantification
  • Network quantification
  • Prevalence estimation

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Graphics and Computer-Aided Design
  • Software

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  • Cite this

    Tang, L., Gao, H., & Liu, H. (2010). Network quantification despite biased labels. In Proceedings of the 8th Workshop on Mining and Learning with Graphs, MLG'10 (pp. 147-154). (Proceedings of the 8th Workshop on Mining and Learning with Graphs, MLG'10). https://doi.org/10.1145/1830252.1830271