Analysis of Link Prediction in Directed and Weighted Social Network Structure

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 683)


The main aim of this paper is to develop algorithms for link prediction based on directed and weighted social network structure. In this paper, the four algorithms such as Modified Common Neighbor (MCN), Modified Jaccard’s Coefficient (MJC), Modified Adamic Adar (MAA) and Modified Preferential Attachment (MPA) has been proposed which is suitable for directed and weighted networks. In our proposed algorithms, the degree of nodes and weightage of each link has been considered. The weightage of each link is assigned using random function. The Modified Common Neighbor (MCN), Modified Jaccard’s Coefficient (MJC), and Modified Adamic Adar (MAA) algorithms are based on an existing Common Neighbor algorithm. The Modified Preferential Attachment (MPA) algorithm depends on the degree of the nodes. The comparative analysis of our proposed algorithms and existing algorithms is performed based on area under receiver operating characteristic values (AUC values), considering different observed links. According to the experimental analysis, it may be concluded that our proposed algorithms provide better performances in comparison to the existing algorithms. Modified Common Neighbor and Modified Adamic Adar results in highest AUC value when twitter dataset and amazon dataset is considered. The proposed algorithms will be applicable in different directed and weighted social network structure for prediction of links between the users.


Social network Link prediction Directed and weighted network Network structure AUC 



This work is supported by the Adhoc and Wireless Sensor Lab under School of Computer & Systems Sciences and DST purse of Jawaharlal Nehru University, India.


  1. 1.
    Zhang, X., Zhao, C., Wang, X., Yi, D.: Identifying missing and spurious interactions in directed networks. Int. J. Distrib. Sens. Netw., 470–481 (2014)Google Scholar
  2. 2.
    Furht, B.: Handbook of Social Network Technologies and Applications. Springer Science & Business Media, New York (2010)CrossRefGoogle Scholar
  3. 3.
    Bliss, C.A., Frank, M.R., Danforth, C.M., Dodds, P.S.: An evolutionary algorithm approach to link prediction in dynamic social networks. J. Comput. Sci. 5(5), 750–764 (2014)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Li, F., He, J., Huang, G., Zhang, Y., Shi, Y.: Retracted: A clustering-based link prediction method in social networks. Procedia Comput. Sci. 432–442 (2014)Google Scholar
  5. 5.
    Li, D., Xu, Z., Li, S., Sun, X.: Link prediction in social networks based on hypergraph. In: 22nd International Conference on World Wide Web, 13 May 2013, pp. 41–42. ACM (2013)Google Scholar
  6. 6.
    Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Assoc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)CrossRefGoogle Scholar
  7. 7.
    Al Hasan, M., Chaoji, V., Salem, S., Zaki, M.: Link prediction using supervised learning. In: SDM06: Workshop on Link Analysis, Counter-Terrorism and Security, 20 April 2006Google Scholar
  8. 8.
    Javari, A., Jalili, M.: Cluster-based collaborative filtering for sign prediction in social networks with positive and negative links. ACM Trans. Intell. Syst. Technol. 5(2), 24 (2014)CrossRefGoogle Scholar
  9. 9.
    Gupta, N., Singh, A.: A novel strategy for link prediction in social networks. CoNEXT on Student Workshop, 2 December 2014, pp. 12–14. ACM (2014)Google Scholar
  10. 10.
    Yu, Y., Wang, X.: Link prediction in directed network and its application in microblog. Math. Prob. Eng. (2014)Google Scholar
  11. 11.
    Fire, M., Tenenboim-Chekina, L., Puzis, R., Lesser, O., Rokach, L., Elovici, Y.: Computationally efficient link prediction in a variety of social networks. ACM Trans. Intell. Syst. Technol. 5(1), 10 (2013)CrossRefGoogle Scholar
  12. 12.
    Lü, L., Zhou, T.: Link prediction in complex networks: a survey. Physica A 390(6), 1150–1170 (2011)CrossRefGoogle Scholar
  13. 13.
    Wang, T., Liao, G.: A review of link prediction in social networks. In: 2014 International Conference on Management of e-Commerce and e-Government (ICMeCG), 31 Oct 2014, pp. 147–150. IEEE (2014)Google Scholar
  14. 14.
    Murata, T., Moriyasu, S.: Link prediction of social networks based on weighted proximity measures. In: IEEE/WIC/ACM International Conference on Web Intelligence, 2 Nov 2007, pp. 85–88. IEEE Computer Society (2007)Google Scholar
  15. 15.
    Güneş, İ., Gündüz-Öğüdücü, Ş., Çataltepe, Z.: Link prediction using time series of neighborhood-based node similarity scores. Data Min. Knowl. Discov. 30(1), 147–180 (2016)MathSciNetCrossRefGoogle Scholar
  16. 16.
    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
  17. 17.
    Mengshoel, O.J., Desai, R., Chen, A., Tran, B.: Will we connect again? Machine learning for link prediction in mobile social networks. In: Eleventh Workshop on Mining and Learning with Graphs (2013)Google Scholar
  18. 18.
    Sett, N., Singh, S.R., Nandi, S.: Influence of edge weight on node proximity based link prediction methods: an empirical analysis. Neurocomputing 172, 71–83 (2016)CrossRefGoogle Scholar
  19. 19.
    Li, J., Zhang, L., Meng, F., Li, F.: Recommendation algorithm based on link prediction and domain knowledge in retail transactions. Procedia Comput. Sci. 31, 875–881 (2014)CrossRefGoogle Scholar
  20. 20.
    Xia, S., Dai, B., Lim, E.P., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: the role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 26 Aug 2012, pp. 153–157. IEEE (2012)Google Scholar
  21. 21.
    Gao, F., Musial, K., Cooper, C., Tsoka, S.: Link prediction methods and their accuracy for different social networks and network metrics. Sci. Program. (2015)Google Scholar

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© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.School of Computer and Systems SciencesJawaharlal Nehru UniversityNew DelhiIndia

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