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International Symposium on Neural Networks

ISNN 2018: Advances in Neural Networks – ISNN 2018 pp 346–354Cite as

Missing Link Prediction in Social Networks

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10878))

Abstract

This paper summarizes our effort of applying matrix completion techniques to a popular social network problem: link prediction. The results of our matrix completion algorithm are comparable or even better than the results of state-of-the-art methods. This means that matrix completion is a promising technique for social network problems. In addition, we customize our algorithm and developed a recommender system for Github. The recommender can help users find software tools that match their interest.

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Acknowledgments

This research was supported by Office of Naval Research under contract # N00014-12-C-0079. Distribution Statement A. Approved for public release; distribution is unlimited.

Author information

Authors and Affiliations

  1. Signal Processing, Inc., Rockville, MD, USA

    Jin Zhou & Chiman Kwan

Authors
  1. Jin Zhou
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  2. Chiman Kwan
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Corresponding author

Correspondence to Chiman Kwan .

Editor information

Editors and Affiliations

  1. Texas A&M University at Qatar, Doha, Qatar

    Tingwen Huang

  2. Sichuan University, Chengdu, China

    Jiancheng Lv

  3. Southeast University, Nanjing, China

    Changyin Sun

  4. United Institute of Informatics Problems, Minsk, Belarus

    Alexander V. Tuzikov

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Cite this paper

Zhou, J., Kwan, C. (2018). Missing Link Prediction in Social Networks. In: Huang, T., Lv, J., Sun, C., Tuzikov, A. (eds) Advances in Neural Networks – ISNN 2018. ISNN 2018. Lecture Notes in Computer Science(), vol 10878. Springer, Cham. https://doi.org/10.1007/978-3-319-92537-0_40

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  • DOI: https://doi.org/10.1007/978-3-319-92537-0_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92536-3

  • Online ISBN: 978-3-319-92537-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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