Abstract
While the accuracy of link prediction has been improved continuously, the utility of the inferred new links is rarely concerned when it comes to information diffusion. This paper defines the utility of links based on average shortest distance and more importantly defines a special type of links named bridge links based on community structure (overlapping or not) of the network. In sociology, bridge links are considered to play a more crucial role in information diffusion across communities. Considering that the accuracy of previous link prediction methods are high in predicting strong ties but not much high in predicting weak ties, we propose a new link prediction method named iBridge, which aims to infer new diffusion paths using biased structural metrics in a supervised learning framework. The experimental results in 3 real online social networks show that iBridge outperforms the traditional supervised link prediction method especially in inferring the bridge links and meantime, the overall performance of predicting bridge links and non-bridge links is not compromised, thus verifying its robustness in inferring new links.
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References
Aggarwal, C.C., Xie, Y., Yu, P.S.: A framework for dynamic link prediction in heterogeneous networks. Stat. Anal. Data Min. ASA Data Sci. J. 7(1), 14–33 (2014)
Ahn, Y.Y., Bagrow, J.P., Lehmann, S.: Link communities reveal multiscale complexity in networks. Nature 466(7307), 761 (2010)
Bakshy, E., Rosenn, I., Marlow, C., Adamic, L.: The role of social networks in information diffusion. In: International Conference on World Wide Web, pp. 519–528 (2012)
Bilgic, M., Mihalkova, L., Getoor, L.: Active learning for networked data. In: International Conference on Machine Learning, pp. 79–86 (2010)
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. 2008(10), 155–168 (2008)
Brouard, C., D’Alché-Buc, F., Szafranski, M.: Semi-supervised penalized output Kernel regression for link prediction. In: International Conference on Machine Learning, pp. 593–600 (2013)
Burt, R.S.: Structural holes and good ideas. Am. J. Soc. 110(2), 349–399 (2004)
Chiu, H.Y., Chen, S.M.: Propagating online social networks: via different kinds of weak ties. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1189–1195 (2013)
Clauset, A., Moore, C., Newman, M.E.: Hierarchical structure and the prediction of missing links in networks. Nature 453(7191), 98 (2008)
Ferrara, E., Meo, P.D., Fiumara, G., Provetti, A.: The role of strong and weak ties in Facebook: a community structure perspective. Commun. ACM 57(11), 78–84 (2012)
Granovetter, M.: The strength of weak ties. Am. J. Soc. 78(6), 1360–1380 (1973)
Hasan, M.A., Chaoji, V., Salem, S., Zaki, M.: Link prediction using supervised learning. In: Proceedings of SDM 2006 Workshop on Link Analysis, Counterterrorism and Security (2006)
Kashima, H., et al.: Link propagation: a fast semi-supervised learning algorithm for link prediction. In: International Conference on World Wide Web, pp. 1099–1110 (2009)
Ke, Q., Ahn, Y.Y.: Tie strength distribution in scientific collaboration networks. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 90(3), 032804 (2014)
Liu, H., Hu, Z., Haddadi, H., Tian, H.: Hidden link prediction based on node centrality and weak ties. Europhys. Lett. 101(1), 18004 (2013)
Lü, L., Zhou, T.: Link prediction in weighted networks: the role of weak ties. Europhys. Lett. 89(1), 18001 (2010)
Lü, L., Zhou, T.: Link prediction in complex networks: a survey. Phys. A Stat. Mech. Appl. 390(6), 1150–1170 (2011)
Meo, P.D., Ferrara, E., Fiumara, G., Provetti, A.: On facebook, most ties are weak. Commun. ACM 57(11), 78–84 (2014)
Newman, M.E.: Fast algorithm for detecting community structure in networks. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 69, 066133 (2004)
Sarukkai, R.R.: Link prediction and path analysis using markov chains. Comput. Netw. 33(1–6), 377–386 (2000)
Shi, C., Li, Y., Zhang, J., Sun, Y., Yu, P.S.: A survey of heterogeneous information network analysis. IEEE Trans. Knowl. Data Eng. 29(1), 17–37 (2017)
Song, C., Hsu, W., Lee, M.L.: Mining brokers in dynamic social networks. In: ACM International on Conference on Information and Knowledge Management, pp. 523–532 (2015)
Xie, J., Szymanski, B.K.: Towards linear time overlapping community detection in social networks. In: Tan, P.-N., Chawla, S., Ho, C.K., Bailey, J. (eds.) PAKDD 2012. LNCS (LNAI), vol. 7302, pp. 25–36. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-30220-6_3
Zhao, J., Wu, J., Xu, K.: Weak ties: subtle role of information diffusion in online social networks. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 82(2), 016105 (2010)
Acknowledgements
This research was supported by the National Nature Science Foundation of China (No. 61571238, No. 61603197 and No. 61772284).
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Zhang, P., Chen, KJ., Wu, T. (2019). Inferring Social Bridges that Diffuse Information Across Communities. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11440. Springer, Cham. https://doi.org/10.1007/978-3-030-16145-3_37
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