Social trust network embedding

Pinghua Xu, Wenbin Hu*, Jia Wu, Weiwei Liu, Bo Du, Jian Yang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

16 Citations (Scopus)


Developing effective network embedding methods for social trust networks (STNs) is a non-trivial problem because two key pieces of information need to be preserved simultaneously: a user's relations to latent factors and the trust transfer patterns that govern what type of relationship will form. In this study, we propose a novel social trust network embedding method (STNE) to address these issues. Specifically, we present a modified Skip-Gram model with negative sampling to jointly learn latent factor features, along with the trust transfer pattern features. Moreover, we define a flexible notion about a user's latent relationships with other users, which generates reliable negative samples for optimization. Extensive experiments on several real-world networks demonstrate the efficacy of the proposed STNE.

Original languageEnglish
Title of host publicationProceedings, 19th IEEE International Conference on Data Mining
EditorsJianyong Wang, Kyuseok Shim, Xindong Wu
Place of PublicationLos Alamitos, CA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages10
ISBN (Electronic)9781728146034
Publication statusPublished - 2019
Event19th IEEE International Conference on Data Mining, ICDM 2019 - Beijing, China
Duration: 8 Nov 201911 Nov 2019

Publication series

NameIEEE International Conference on Data Mining
ISSN (Print)1550-4786


Conference19th IEEE International Conference on Data Mining, ICDM 2019


  • Network embedding
  • Social network


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