Abstract
One of the most important work to analyse online social networks is link mining. A new type of social networks with positive and negative relationships are burgeoning. We present a link mining method based on random walk theory to mine the unknown relationships in directed social networks which have negative relationships. Firstly, we define an extended Laplacian matrix based on this type of social networks. Then, we prove the matrix can be used to compute the similarities of the node pairs. Finally, we propose a link mining method based on collaboration recommendation method. We apply our method in two real social networks. Experimental results show that our method do better in terms of sign accuracy and AUC for mining unknown links in the two real datasets.
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References
Leicht, E.A., Holme, P., Newman, M.E.J.: Vertex similarity in networks. Phys. Rev. E 73(3), 026120–026130 (2006)
Sarukkai, R.R.: Link prediction and path analysis using markov chains. Comput. Netw. 33(1–6), 377–386 (2000)
Guha, R., Kumar, R., Raghavan, P., Tomkins, A.: Propagation of trust and distrust. In: Proceedings of the 13th International Conference on World Wide Web, pp. 403–412. ACM Press (2004) (doi:10.1145/988672.988727)
Kunegis, J., Lommatzsch, A., Bauckhage, C.: The Slashdot Zoo: mining a social network with negative edes. In: Proceedings of the 18th International Conference on World Wide Web, ESP, pp. 741–750. ACM Press (2009) (doi:10.1145/1526709.1526809)
Kunegis, J., Preusse, J., Schwagereit, F.: What is the added value of negative links in online social networks? In: Proceedings of the 22nd International Conference on World Wide Web, pp. 727–736 (2013)
Leskovec, J., Huttenlocher, D., Kleinberg, J.: Predicting positive and negative links in online social networks. In: Proceedings of the 19th International Conference on World Wide Web, pp. 641–650 (2010)
Chiang, K.-Y., Hsieh, C.-J., Natarajan, N., Dhillon, I.S., Tewari, I.S.: Prediction and clustering in signed networks: a local to global Perspective. J. Mach. Learn. Res. 15(1), 1177–1213 (2014)
Fouss, F., Pirotie, A., Renders, J.M., et al.: Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans. Knowl. Data Eng. 19(3), 355–369 (2007)
Cartwright, D., Harary, F.: Structure balance: a generalization of Heiders theory. Psych. Rev. 63(5), 277–293 (1956)
Hiley, B.J., Peat, F.D.: Quantum Implications: Essays in Honour of David Bohm. Psychology Press, London (1991)
Chung, F.: Laplacians and the Cheeger inequality for directed graphs. Ann. Comb. 9(1), 1–19 (2005)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Comput. 42(1), 30–37 (2009)
Symeonidis, P., Tiakas, E., Manolopoulos, Y.: Transitive node similarity for link prediction in social networks with positive and negative links. In: Proceedings of the 2010 ACM Conference on Recommender Systems, pp. 183–190 (2010)
Wang, H., Yuan, W., Yu, X.: Bi-direction link prediction in dynamic multi-dimension networks. J. Comput. Inform. Syst. 10(3), 1333–1340 (2014)
Zheng, Q., Skillicorn, D.B.: Spectral embedding of signed networks. In: SDM (2015)
Acknowledgement
This work is supported by National Natural Science Foundation under Grant (No. 61373149, 61472233, 61572300), Technology Program of Shandong Province under Grant (No.2014GGB01617, ZR2014FM001), Taishan Scholar Program of Shandong Procince(No.TSHW201502038), Exquisite course project of Shandong Province (No. 2012BK294, 2013BK399, and 2013BK402), and Education scientific planning project of Shandong province (No. ZK1437B010).
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Hu, B., Wang, H. (2016). Link Mining in Online Social Networks with Directed Negative Relationships. In: Che, W., et al. Social Computing. ICYCSEE 2016. Communications in Computer and Information Science, vol 623. Springer, Singapore. https://doi.org/10.1007/978-981-10-2053-7_38
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DOI: https://doi.org/10.1007/978-981-10-2053-7_38
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