Empirical comparison of "hard" and "soft" label propagation for relational classification

Aram Galstyan, Paul R. Cohen

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

2 Scopus citations


In this paper we differentiate between hard and soft label propagation for classification of relational (networked) data. The latter method assigns probabilities or class-membership scores to data instances, then propagates these scores throughout the networked data, whereas the former works by explicitly propagating class labels at each iteration. We present a comparative empirical study of these methods applied to a relational binary classification task, and evaluate two approaches on both synthetic and real-world relational data. Our results indicate that while neither approach dominates the other over the entire range of input data parameters, there are some interesting and non-trivial tradeoffs between them.

Original languageEnglish (US)
Title of host publicationInductive Logic Programming - 17th International Conference, ILP 2007, Revised Selected Papers
Number of pages14
StatePublished - 2008
Externally publishedYes
Event17th International Conference on Inductive Logic Programming, ILP 2007 - Corvallis, OR, United States
Duration: Jun 19 2007Jun 21 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4894 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other17th International Conference on Inductive Logic Programming, ILP 2007
Country/TerritoryUnited States
CityCorvallis, OR

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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