Link prediction across networks by biased cross-network sampling

Guo Jun Qi, Charu C. Aggarwal, Thomas Huang

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

Abstract

The problem of link inference has been widely studied in a variety of social networking scenarios. In this problem, we wish to predict future links in a growing network with the use of the existing network structure. However, most of the existing methods work well only if a significant number of links are already available in the network for the inference process. In many scenarios, the existing network may be too sparse, and may have too few links to enable meaningful learning mechanisms. This paucity of linkage information can be challenging for the link inference problem. However, in many cases, other (more densely linked) networks may be available which show similar linkage structure in terms of underlying attribute information in the nodes. The linkage information in the existing networks can be used in conjunction with the node attribute information in both networks in order to make meaningful link recommendations. Thus, this paper introduces the use of transfer learning methods for performing cross-network link inference. We present experimental results illustrating the effectiveness of the approach.

Original languageEnglish (US)
Title of host publicationICDE 2013 - 29th International Conference on Data Engineering
Pages793-804
Number of pages12
DOIs
StatePublished - 2013
Event29th International Conference on Data Engineering, ICDE 2013 - Brisbane, QLD, Australia
Duration: Apr 8 2013Apr 11 2013

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627

Other

Other29th International Conference on Data Engineering, ICDE 2013
CountryAustralia
CityBrisbane, QLD
Period4/8/134/11/13

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

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