Link prediction with signed latent factors in signed social networks

Pinghua Xu, Wenbin Hu*, Jia Wu, Bo Du

*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

Link prediction in signed social networks is an important and challenging problem in social network analysis. To produce the most accurate prediction results, two questions must be answered: (1) Which unconnected node pairs are likely to be connected by a link in future? (2) What will the signs of the new links be? These questions are challenging, and current research seldom well solves both issues simultaneously. Additionally, neutral social relationships, which are common in many social networks can affect the accuracy of link prediction. Yet neutral links are not considered in most existing methods. Hence, in this paper, we propose a signed latent factor (SLF) model that answers both these questions and, additionally, considers four types of relationships: positive, negative, neutral and no relationship at all. The model links social relationships of different types to the comprehensive, but opposite, effects of positive and negative SLFs. The SLF vectors for each node are learned by minimizing a negative log-likelihood objective function. Experiments on four real-world signed social networks support the efficacy of the proposed model.

Original languageEnglish
Title of host publicationKDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Pages1046-1054
Number of pages9
ISBN (Electronic)9781450362016
DOIs
Publication statusPublished - 2019
Event25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019 - Anchorage, United States
Duration: 4 Aug 20198 Aug 2019

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019
CountryUnited States
CityAnchorage
Period4/08/198/08/19

Keywords

  • Link prediction
  • Signed latent factor
  • Signed social network

Fingerprint Dive into the research topics of 'Link prediction with signed latent factors in signed social networks'. Together they form a unique fingerprint.

  • Cite this

    Xu, P., Hu, W., Wu, J., & Du, B. (2019). Link prediction with signed latent factors in signed social networks. In KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1046-1054). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). New York, NY: Association for Computing Machinery (ACM). https://doi.org/10.1145/3292500.3330850