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Complex Networks VI pp 99–110Cite as

Neighbourhood Distinctiveness: An Initial Study

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 597))

Abstract

We investigate the potential for using neighbourhood attributes alone, to match unidentified entities across networks, and to classify them within networks. The motivation is to identify individuals across the dark social networks that underly recorded networks. We test an Enron email database and show the out-neighbourhoods of email addresses are highly distinctive. Then, using citation databases as proxies, we show that a paper in CiteSeer which is also in DBLP, is highly likely to be matched successfully, based on its (uncertainly labelled) in-neighbours alone. A paper in SPIRES can be classified with 80% accuracy, based on classification ratios in its in-neighbourhood alone.

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Author information

Authors and Affiliations

  1. RMIT University, Melbourne, Australia

    A. Hecker, C. J. Carstens & K. J. Horadam

Authors
  1. A. Hecker
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  2. C. J. Carstens
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  3. K. J. Horadam
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Editor information

Editors and Affiliations

  1. Dip. di Ingegneria Elettrica, Elettronica e Informatica, University of Catania, Catania, Italy

    Giuseppe Mangioni

  2. Dept. of Engineering Mathematics, University of Bristol, Clifton, United Kingdom

    Filippo Simini

  3. New York Hall of Science, New York, New York, USA

    Stephen Miles Uzzo

  4. College of Information Sciences and Technology, Pennsylvania State University, University Park, New York, USA

    Dashun Wang

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© 2015 Springer International Publishing Switzerland

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Hecker, A., Carstens, C.J., Horadam, K.J. (2015). Neighbourhood Distinctiveness: An Initial Study. In: Mangioni, G., Simini, F., Uzzo, S., Wang, D. (eds) Complex Networks VI. Studies in Computational Intelligence, vol 597. Springer, Cham. https://doi.org/10.1007/978-3-319-16112-9_10

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  • DOI: https://doi.org/10.1007/978-3-319-16112-9_10

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16111-2

  • Online ISBN: 978-3-319-16112-9

  • eBook Packages: EngineeringEngineering (R0)

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