Statistical link label modeling for sign prediction: Smoothing sparsity by joining local and global information

Amin Javari, Hongxiang Qiu, Elham Barzegaran, Mahdi Jalili, Kevin Chen Chuan Chang

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

Abstract

One of the major issues in signed networks is to use network structure to predict the missing sign of an edge. In this paper, we introduce a novel probabilistic approach for the sign prediction problem. The main characteristic of the proposed models is their ability to adapt to the sparsity level of an input network. Building a model that has an ability to adapt to the sparsity of the data has not yet been considered in the previous related works. We suggest that there exists a dilemma between local and global structures and attempt to build sparsity adaptive models by resolving this dilemma. To this end, we propose probabilistic prediction models based on local and global structures and integrate them based on the concept of smoothing. The model relies more on the global structures when the sparsity increases, whereas it gives more weights to the information obtained from local structures for low levels of the sparsity. The proposed model is assessed on three real-world signed networks, and the experiments reveal its consistent superiority over the state of the art methods. As compared to the previous methods, the proposed model not only better handles the sparsity problem, but also has lower computational complexity and can be updated using real-time data streams.

Original languageEnglish (US)
Title of host publicationProceedings - 17th IEEE International Conference on Data Mining, ICDM 2017
EditorsGeorge Karypis, Srinivas Alu, Vijay Raghavan, Xindong Wu, Lucio Miele
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1039-1044
Number of pages6
ISBN (Electronic)9781538638347
DOIs
StatePublished - Dec 15 2017
Event17th IEEE International Conference on Data Mining, ICDM 2017 - New Orleans, United States
Duration: Nov 18 2017Nov 21 2017

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2017-November
ISSN (Print)1550-4786

Other

Other17th IEEE International Conference on Data Mining, ICDM 2017
CountryUnited States
CityNew Orleans
Period11/18/1711/21/17

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Keywords

  • Global structure
  • Link label prediction
  • Local structure
  • Signed networks
  • Smoothing

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Javari, A., Qiu, H., Barzegaran, E., Jalili, M., & Chang, K. C. C. (2017). Statistical link label modeling for sign prediction: Smoothing sparsity by joining local and global information. In G. Karypis, S. Alu, V. Raghavan, X. Wu, & L. Miele (Eds.), Proceedings - 17th IEEE International Conference on Data Mining, ICDM 2017 (pp. 1039-1044). (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 2017-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2017.135