Abstract

Relational learning has been proposed to cope with the interdependency among linked instances in social network analysis, which often adopts network connectivity and social media content for prediction. A common assumption in existing relational learning methods is that data instances are equally important. The algorithms developed based on the assumption may be significantly affected by outlier data and thus less robust. In the meantime, it has been well established in social sciences that actors are naturally of different social status in a social network. Motivated by findings from social sciences, in this paper, we investigate whether social status analysis could facilitate relational learning. Particularly, we propose a novel framework RESA to model social status using the network structure. It extracts robust and intrinsic latent social dimensions for social actors, which are further exploited as features for supervised learning models. The proposed method is applicable for real-world relational learning problems where noise exists. Extensive experiments are conducted on datasets obtained from real-world social media platforms. Empirical results demonstrate the effectiveness of RESA and further experiments are conducted to help understand the effects of parameter settings to the proposed model and how local social status works.

Original languageEnglish (US)
Title of host publicationWSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages513-522
Number of pages10
ISBN (Print)9781450337168
DOIs
StatePublished - Feb 8 2016
Event9th ACM International Conference on Web Search and Data Mining, WSDM 2016 - San Francisco, United States
Duration: Feb 22 2016Feb 25 2016

Other

Other9th ACM International Conference on Web Search and Data Mining, WSDM 2016
CountryUnited States
CitySan Francisco
Period2/22/162/25/16

Fingerprint

Social sciences
Supervised learning
Electric network analysis
Experiments

Keywords

  • Relational learning
  • Social dimensions
  • Social media

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Computer Networks and Communications

Cite this

Wu, L., Hu, X., & Liu, H. (2016). Relational learning with social status analysis. In WSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining (pp. 513-522). Association for Computing Machinery, Inc. https://doi.org/10.1145/2835776.2835782

Relational learning with social status analysis. / Wu, Liang; Hu, Xia; Liu, Huan.

WSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, Inc, 2016. p. 513-522.

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

Wu, L, Hu, X & Liu, H 2016, Relational learning with social status analysis. in WSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, Inc, pp. 513-522, 9th ACM International Conference on Web Search and Data Mining, WSDM 2016, San Francisco, United States, 2/22/16. https://doi.org/10.1145/2835776.2835782
Wu L, Hu X, Liu H. Relational learning with social status analysis. In WSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, Inc. 2016. p. 513-522 https://doi.org/10.1145/2835776.2835782
Wu, Liang ; Hu, Xia ; Liu, Huan. / Relational learning with social status analysis. WSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, Inc, 2016. pp. 513-522
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