Using community information to improve the precision of link prediction methods

Sucheta Soundarajan, John Hopcroft

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

62 Scopus citations

Abstract

Because network data is often incomplete, researchers consider the link prediction problem, which asks which nonexistent edges in an incomplete network are most likely to exist in the complete network. Classical approaches compute the 'similarity' of two nodes, and conclude that highly similar nodes are most likely to be connected in the complete network. Here, we consider several such similarity-based measures, but supplement the similarity calculations with community information. We show that for many networks, the inclusion of community information improves the accuracy of similarity-based link prediction methods. Copyright is held by the author/owner(s).

Original languageEnglish (US)
Title of host publicationWWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion
Pages607-608
Number of pages2
DOIs
StatePublished - May 21 2012
Externally publishedYes
Event21st Annual Conference on World Wide Web, WWW'12 - Lyon, France
Duration: Apr 16 2012Apr 20 2012

Publication series

NameWWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion

Other

Other21st Annual Conference on World Wide Web, WWW'12
CountryFrance
CityLyon
Period4/16/124/20/12

Keywords

  • Communities
  • Link prediction
  • Social networks

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Soundarajan, S., & Hopcroft, J. (2012). Using community information to improve the precision of link prediction methods. In WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion (pp. 607-608). (WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion). https://doi.org/10.1145/2187980.2188150