Iterative relational classification through three-state epidemic dynamics

Aram Galstyan, Paul R. Cohen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Relational classification in networked data plays an important role in many problems such as text categorization, classification of web pages, group finding in peer networks, etc. We have previously demonstrated that for a class of label propagating algorithms the underlying dynamics can be modeled as a two-state epidemic process on heterogeneous networks, where infected nodes correspond to classified data instances. We have also suggested a binary classification algorithm that utilizes non-trivial characteristics of epidemic dynamics. In this paper we extend our previous work by considering a three-state epidemic model for label propagation. Specifically, we introduce a new, intermediate state that corresponds to "susceptible" data instances. The utility of the added state is that it allows to control the rates of epidemic spreading, hence making the algorithm more flexible. We show empirically that this extension improves significantly the performance of the algorithm. In particular, we demonstrate that the new algorithm achieves good classification accuracy even for relatively large overlap across the classes.

Original languageEnglish (US)
Title of host publicationIntelligence and Security Informatics - IEEE International Conference on Intelligence and Security Informatics, ISI 2006, Proceedings
Number of pages10
ISBN (Print)3540344780, 9783540344780
StatePublished - 2006
Externally publishedYes
EventIEEE International Conference on Intelligence and Security Informatics, ISI 2006 - San Diego, CA, United States
Duration: May 23 2006May 24 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3975 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherIEEE International Conference on Intelligence and Security Informatics, ISI 2006
Country/TerritoryUnited States
CitySan Diego, CA


  • Binary classification
  • Relational learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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