Improve Link Prediction Accuracy with Node Attribute Similarities

  • Yinuo ZhangEmail author
  • Subin Shen
  • Zhenyu Wu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 905)


Link prediction is one of the significant research problems in social networks analysis. Most previous works in this area neglect attribute similarity of the node pair which can easily obtain from real world dataset. Traditional supervised learning methods study the link prediction problem as a binary classification problem, where features are extracted from topology of the network. In this paper, we propose a similarity index called Attribute Proximity. The set of features are similarity index we proposed and four others well-known neighbourhood based features. We then apply a supervised learning based temporal link prediction framework on DBLP dataset and examine whether attribute similarity feature can improve the performance of the link prediction. In our experiments, the AUC performance is better when attribute similarity feature is considered.


Link prediction Social networks Node attributes 



This work is supported by National Natural Science Foundation of China (No. 61502246), NUPTSF (No. NY215019).


  1. 1.
    Liben-Nowell, D., Kleinberg, J.: The Link-Prediction Problem for Social Networks, vol. 58, pp. 1019–1031. Wiley, New York (2007)CrossRefGoogle Scholar
  2. 2.
    Yu, H.Y., Braun, P., Yildirim, M.A., Lemmens, I., Venkatesan, K.: High-quality binary protein interaction map of the yeast interactome network. Science 322(5898), 104–110 (2008)CrossRefGoogle Scholar
  3. 3.
    Schafer, J.B., Konstan, J.A., Riedl, J.: E-commerce recommendation applications. Data Min. Knowl. Disc. 5(1–2), 115–153 (2001)CrossRefGoogle Scholar
  4. 4.
    Hasan, M.A.: Link prediction using supervised learning. In: Proceedings of SDM Workshop on Link Analysis Counterterrorism & Security, vol. 30(9), pp. 798–805 (2006)Google Scholar
  5. 5.
    Soares, P.R.D.S., Prudêncio, R.B.C.: Time series based link prediction. In: International Joint Conference on Neural Networks, vol. 20, pp. 1–7. IEEE (2012)Google Scholar
  6. 6.
    Barabási, A.L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., Vicsek, T.: Evolution of the social network of scientific collaborations. Phys. A 311(3), 590–614 (2001)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Newman, M.E.J.: Clustering and preferential attachment in growing networks. Phys. Rev. E 64(2), 025102 (2001)CrossRefGoogle Scholar
  8. 8.
    Salton, G., Mcgill, M.J.: Introduction to Modern Information Retrieval, vol. 41, pp. 305–306. McGraw-Hill (1983)Google Scholar
  9. 9.
    Adamic, L.A., Adar, E.: Friends and neighbors on the Web. Soc. Netw. 25(3), 211–230 (2003)CrossRefGoogle Scholar
  10. 10.
    Makridakis, S.G., Wheelwright, S.C.: Forecasting Methods for Management, vol. 15, pp. 345–349. Wiley (1985)Google Scholar
  11. 11.
    Duan, L., Aggarwal, C., Ma, S., Hu, R., Huai, J.: Scaling up link prediction with ensembles. In: ACM International Conference on Web Search and Data Mining, pp. 367–376. ACM (2016)Google Scholar
  12. 12.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)MathSciNetCrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.School of IOTNanjing University of Posts and TelecommunicationsNanjingChina

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