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)


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.


local structure neighbourhood matching instance matching 


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  1. 1.
    Aalseth, C.E., et al.: Neutrinoless double-β decay of 76Ge: First results from the International Germanium Experiment (IGEX) with six isotopically enriched detectors. Phys. Rev. C 59, 2108–2113 (1999)CrossRefGoogle Scholar
  2. 2.
    Adamic, L.A., Adar, E.: Friends and neighbours on the Web. Social Networks 25, 211–230 (2003)CrossRefGoogle Scholar
  3. 3.
    Artzy-Randrup, Y., Fleishman, S., Ben-Tal, N., Stone, L.: Comment on “Network motifs: simple building blocks of complex networks” and “superfamilies of evolved and designed networks”. Science 305(5687), 1107 (2004)CrossRefGoogle Scholar
  4. 4.
    Bunke, H., Dickinson, P.J., Kraetzl, M., Wallis, W.D.: A graph-theoretic approach to enterprise network dynamics. Birkhäuser (2007)Google Scholar
  5. 5.
    Carstens, C.J.: A uniform random graph model for directed acyclic networks and its effect on finding motifs. J. Complex Networks 2, 419–430 (2014)CrossRefGoogle Scholar
  6. 6.
    Castano, S., Ferrara, A., Montanelli, S., Varese, G.: Ontology and instance matching. In: Paliouras, G., Spyropoulos, C.D., Tsatsaronis, G. (eds.) Multimedia Information Extraction. LNCS, vol. 6050, pp. 167–195. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
  8. 8.
    Cunningham, P., Harrigan, M., Wu, G., O’Callaghan, D.: Characterizing ego-networks using motifs. Network Science 1(2), 170–190 (2013)CrossRefGoogle Scholar
  9. 9.
  10. 10.
    Everton, S.F.: Disrupting dark networks. Cambridge University Press (2012)Google Scholar
  11. 11.
    Gunion, J.F., Willey, R.S.: Hadronic spectroscopy for a linear quark containment potential. Phys. Rev. D 12(1), 174–186 (1975)CrossRefGoogle Scholar
  12. 12.
    Holland, P., Leinhardt, S.: Local structure in social networks. Sociological Methodology 7(1), 1–45 (1976)CrossRefGoogle Scholar
  13. 13.
    Jeffers, J., Horadam, K.J., Carstens, C.J., Rao, A., Boztaş, S.: Influence neighbourhoods in CiteSeer: a case study. In: Proc. SITIS 2013, pp. 612–618. IEEE/ACM (2013)Google Scholar
  14. 14.
    Lacoste-Julien, S., et al.: SiGMa: Simple greedy matching for aligning large knowledge bases. In: KDD 2013, pp. 572–580 (2013)Google Scholar
  15. 15.
    Lehmann, S., Lautrup, B., Jackson, A.D.: Citation networks in high energy physics. Phys. Rev. E 68(2), 026113 (2003)CrossRefGoogle Scholar
  16. 16.
    Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks. Science 298(5594), 824–827 (2002)CrossRefGoogle Scholar
  17. 17.
    Narayana, A., Shmatikov, V.: De-anonymizing social networks. In: 2009 IEEE Symposium on Security and Privacy, pp. 173–187 (2009)Google Scholar
  18. 18.
    Pedarsani, P., Figueiredo, D., Grossglauser, M.: A Bayesian method for matching two similar graphs without seeds. In: IEEE 51st Allerton Conference, pp. 1598–1607 (2013)Google Scholar
  19. 19.
    Roobaert, D., Karakoulas, G., Chawla, N.: Information gain, correlation and support vector machines. In: Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L.A. (eds.) Feature Extraction. STUDFUZZ, vol. 207, pp. 463–470. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  20. 20.
    Saul, Z.M., Filkov, V.: Exploring biological network structure using exponential random graph models. Bioinformatics 23(19), 2604–2611 (2007)CrossRefGoogle Scholar
  21. 21.
    Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: ArnetMiner: Extraction and Mining of Academic Social Networks. In: KDD 2008, pp. 990–998 (2008),
  22. 22.
    Xiao, Y., Xiong, M., Wang, W., Wang, H.: Emergence of symmetry in complex networks. Phys. Rev. E 77, 066108 (2008)CrossRefMathSciNetGoogle Scholar
  23. 23.
    Zhou, T., Lü, L., Zhang, Y.-C.: Predicting missing links via local information. Eur. Phys. J. B 71, 623–630 (2009)CrossRefMATHGoogle Scholar

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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