Neighbourhood Distinctiveness: An Initial Study

  • A. Hecker
  • C. J. Carstens
  • K. J. Horadam
Conference paper
Part of the Studies in Computational Intelligence book series (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.

Keywords

local structure neighbourhood matching instance matching 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • A. Hecker
    • 1
  • C. J. Carstens
    • 1
  • K. J. Horadam
    • 1
  1. 1.RMIT UniversityMelbourneAustralia

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