Abstract
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.
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References
Zhang, X., Zhao, C., Wang, X., Yi, D.: Identifying missing and spurious interactions in directed networks. Int. J. Distrib. Sens. Netw., 470–481 (2014)
Furht, B.: Handbook of Social Network Technologies and Applications. Springer Science & Business Media, New York (2010)
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)
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)
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)
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Assoc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)
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 2006
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)
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)
Yu, Y., Wang, X.: Link prediction in directed network and its application in microblog. Math. Prob. Eng. (2014)
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)
Lü, L., Zhou, T.: Link prediction in complex networks: a survey. Physica A 390(6), 1150–1170 (2011)
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)
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)
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)
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)
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)
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)
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)
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)
Gao, F., Musial, K., Cooper, C., Tsoka, S.: Link prediction methods and their accuracy for different social networks and network metrics. Sci. Program. (2015)
Acknowledgment
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.
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Devi, S.J., Singh, B. (2018). Analysis of Link Prediction in Directed and Weighted Social Network Structure. In: Thampi, S., Mitra, S., Mukhopadhyay, J., Li, KC., James, A., Berretti, S. (eds) Intelligent Systems Technologies and Applications. ISTA 2017. Advances in Intelligent Systems and Computing, vol 683. Springer, Cham. https://doi.org/10.1007/978-3-319-68385-0_1
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