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Unsupervised Network Alignment

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Broad Learning Through Fusions

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Abstract

Identifying the common users shared by different online social sites is a very hard task even for humans. Manually labeling of the anchor links can be extremely challenging, expensive (in human efforts, time, and money costs), and tedious, and the scale of the real-world online social networks involving millions even billions of users also renders the training data labeling much more difficult. In this chapter, we will introduce several approaches to resolve the network alignment problem based on the unsupervised learning setting instead, where no labeled training data will be needed in model building.

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Notes

  1. 1.

    https://developers.google.com/maps/.

  2. 2.

    https://www.bingmapsportal.com.

  3. 3.

    https://developer.apple.com/maps/.

  4. 4.

    https://docs.python.org/2/library/datetime.html.

  5. 5.

    https://www.tutorialspoint.com/java/util/java_util_date.htm.

  6. 6.

    http://docs.scipy.org/doc/scipy/reference/optimize.html.

  7. 7.

    http://www.gnu.org/software/glpk/.

  8. 8.

    http://www.lpzoo.org.

  9. 9.

    http://www.scarletbarchicago.com.

  10. 10.

    http://docs.scipy.org/doc/scipy/reference/optimize.html.

  11. 11.

    http://www.gnu.org/software/glpk/.

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Authors and Affiliations

  1. Department of Computer Science, Florida State University, Tallahassee, FL, USA

    Jiawei Zhang

  2. Department of Computer Science, University of Illinois, Chicago, IL, USA

    Philip S. Yu

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  1. Jiawei Zhang
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  2. Philip S. Yu
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Zhang, J., Yu, P.S. (2019). Unsupervised Network Alignment. In: Broad Learning Through Fusions. Springer, Cham. https://doi.org/10.1007/978-3-030-12528-8_5

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  • DOI: https://doi.org/10.1007/978-3-030-12528-8_5

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  • Print ISBN: 978-3-030-12527-1

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