Graph regularized meta-path based transductive regression in heterogeneous information network

Mengting Wan, Yunbo Ouyang, Lance Kaplan, Jiawei Han

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

Abstract

A number of real-world networks are heterogeneous information networks, which are composed of different types of nodes and links. Numerical prediction in heterogeneous information networks is a challenging but significant area because network based information for unlabeled objects is usually limited to make precise estimations. In this paper, we consider a graph regularized meta-path based transductive regression model (Grempt), which combines the principal philosophies of typical graph-based transductive classification methods and transductive regression models designed for homogeneous networks. The computation of our method is time and space efficient and the precision of our model can be verified by numerical experiments.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining 2015, SDM 2015
EditorsJieping Ye, Suresh Venkatasubramanian
PublisherSociety for Industrial and Applied Mathematics Publications
Pages918-926
Number of pages9
ISBN (Electronic)9781510811522
StatePublished - Jan 1 2015
EventSIAM International Conference on Data Mining 2015, SDM 2015 - Vancouver, Canada
Duration: Apr 30 2015May 2 2015

Publication series

NameSIAM International Conference on Data Mining 2015, SDM 2015

Other

OtherSIAM International Conference on Data Mining 2015, SDM 2015
CountryCanada
CityVancouver
Period4/30/155/2/15

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Software

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  • Cite this

    Wan, M., Ouyang, Y., Kaplan, L., & Han, J. (2015). Graph regularized meta-path based transductive regression in heterogeneous information network. In J. Ye, & S. Venkatasubramanian (Eds.), SIAM International Conference on Data Mining 2015, SDM 2015 (pp. 918-926). (SIAM International Conference on Data Mining 2015, SDM 2015). Society for Industrial and Applied Mathematics Publications.