Constructed Link Prediction Model by Relation Pattern on the Social Network

  • Jimmy Ming-Tai Wu
  • Meng-Hsiun TsaiEmail author
  • Tu-Wei Li
  • Hsien-Chung Huang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)


For the link prediction problem, it commonly estimates the similarity by different similarity metrics or machine learning prediction model. However, this paper proposes an algorithm, which is called Relation Pattern Deep Learning Classification (RPDLC) algorithm, based on two neighbor-based similarity metrics and convolution neural network. First, the RPDLC extracts the features for two nodes in a pair, which is calculated with neighbor-based metric and influence nodes. Second, the RPDLC combines the features of nodes to be a heat map for evaluating the similarity of the node’s relation pattern. Third, the RPDLC constructs the prediction model for predicting missing relationship by using convolution neural network architecture. In consequence, the contribution of this paper is purposed a novel approach for link prediction problem, which is used convolution neural network and features by relation pattern to construct a prediction model.


Link prediction problem Convolution neural network Relation pattern Social network 


  1. 1.
    Barbieri, N., Bonchi, F., Manco, G.: Who to follow and why: link prediction with explanations. In: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1266–1275. ACM (2014)Google Scholar
  2. 2.
    Tang, J., Chang, S., Aggarwal, C., Liu, H.: Negative link prediction in social media. In: The Eighth ACM International Conference on Web Search and Data Mining, pp. 87–96. ACM (2015)Google Scholar
  3. 3.
    Zhou, J., Kwan, C.: Missing link prediction in social networks. In: International Symposium on Neural Networks, pp. 346–354. Springer (2018)Google Scholar
  4. 4.
    Hristova, D., Noulas, A., Brown, C., Musolesi, M., Mascolo, C.: A multilayer approach to multiplexity and link prediction in online geo-social networks. EPJ Data Sci. 5(1), 24 (2016)CrossRefGoogle Scholar
  5. 5.
    Sherkat, E., Rahgozar, M., Asadpour, M.: Structural link prediction based on ant colony approach in social networks. Phys. A Stat. Mech. Appl. 419, 80–94 (2015)CrossRefGoogle Scholar
  6. 6.
    Duan, L., Ma, S., Aggarwal, C., Ma, T., Huai, J.: An ensemble approach to link prediction. IEEE Trans. Knowl. Data Eng. 29(11), 2402–2416 (2017)CrossRefGoogle Scholar
  7. 7.
    Leskovec, J., Huttenlocher, D., Kleinberg, J.: Predicting positive and negative links in online social networks. In: The 19th International Conference on World Wide Web, pp. 641–650. ACM (2010)Google Scholar
  8. 8.
    Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Assoc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)CrossRefGoogle Scholar
  9. 9.
    Liu, H., Hu, Z., Haddadi, H., Tian, H.: Hidden link prediction based on node centrality and weak ties. EPL (Europhys. Lett.) 101(1), 18004 (2013)CrossRefGoogle Scholar
  10. 10.
    Cartwright, D., Harary, F.: Structural balance: a generalization of heider’s theory. Psychol. Rev. 63(5), 277 (1956)CrossRefGoogle Scholar
  11. 11.
    Pirouz, M., Zhan, J., Tayeb, S.: An optimized approach for community detection and ranking. J. Big Data 3(1), 22 (2016)CrossRefGoogle Scholar
  12. 12.
    Scellato, S., Noulas, A., Mascolo, C.: Exploiting place features in link prediction on location-based social networks. In: The 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1046–1054. ACM (2011)Google Scholar
  13. 13.
    De Sá, H.R., Prudêncio, R.B.: Supervised link prediction in weighted networks. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 2281–2288. IEEE (2011)Google Scholar
  14. 14.
    Li, X., Chen, H.: Recommendation as link prediction in bipartite graphs: a graph kernel-based machine learning approach. Decis. Supp. Syst. 54(2), 880–890 (2013)CrossRefGoogle Scholar
  15. 15.
    Hua, T.-D., Nguyen-Thi, A.-T., Nguyen, T.-A.H.: Link prediction in weighted network based on reliable routes by machine learning approach. In: 2017 4th NAFOSTED Conference on Information and Computer Science, pp. 236–241. IEEE (2017)Google Scholar
  16. 16.
    Yu, X., Chu, T.: Dynamic link prediction using restricted Boltzmann machine. In: 2017 Chinese Automation Congress (CAC), pp. 4089–4092. IEEE (2017)Google Scholar
  17. 17.
    Sørensen, T.: A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on danish commons. Biol. Skr. 5, 1–34 (1948)Google Scholar
  18. 18.
    Zhou, T., Lü, L., Zhang, Y.-C.: Predicting missing links via local information. Euro. Phys. J. B 71(4), 623–630 (2009)CrossRefGoogle Scholar
  19. 19.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  20. 20.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  21. 21.
    Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. CoRR, abs/1706.02515 (2017)Google Scholar
  22. 22.
    Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetzbMATHGoogle Scholar
  23. 23.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  24. 24.
    Gleiser, P.M., Danon, L.: Community structure in jazz. Adv. Complex Syst. 6(4), 565–573 (2003)CrossRefGoogle Scholar
  25. 25.
    Newman, M.E.J.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74 (2006). arXiv: physics/0605087
  26. 26.
    McAuley, J., Leskovec, J.: Learning to discover social circles in ego networks. In: NIPS, p. 9 (2012)Google Scholar
  27. 27.
    Hanley, J.A., McNeil, B.J.: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1), 29–36 (1982)CrossRefGoogle Scholar
  28. 28.
    Chen, J., Geyer, W., Dugan, C., Muller, M., Guy, I.: Make new friends, but keep the old: recommending people on social networking sites. In: The SIGCHI Conference on Human Factors in Computing Systems, pp. 201–210. ACM (2009)Google Scholar
  29. 29.
    Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003)CrossRefGoogle Scholar
  30. 30.
    Pan, J.-Y., Yang, H.-J., Faloutsos, C., Duygulu, P.: Automatic multimedia cross-modal correlation discovery. In: The Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 653–658. ACM (2004)Google Scholar
  31. 31.
    Papadimitriou, A., Symeonidis, P., Manolopoulos, Y.: Fast and accurate link prediction in social networking systems. J. Syst. Softw. 85(9), 2119–2132 (2012)CrossRefGoogle Scholar
  32. 32.
    Yu, C., Zhao, X., An, L., Lin, X.: Similarity-based link prediction in social networks: a path and node combined approach. J. Inf. Sci. 43(5), 683–695 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jimmy Ming-Tai Wu
    • 1
  • Meng-Hsiun Tsai
    • 2
    Email author
  • Tu-Wei Li
    • 2
  • Hsien-Chung Huang
    • 3
  1. 1.College of Computer Science and EngineeringShandong University of Science and TechnologyQindaoChina
  2. 2.Department of Management Information SystemsNational Chung Hsing UniversityTaichungTaiwan
  3. 3.Office of Physical Education and SportNational Chung Hsing UniversityTaichungTaiwan

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