Abstract
Link Prediction can make networks more complete. However, because of restraint of algorithm, traditional link-prediction measures cannot make full use of weight information to analyze the network. To solve this problem, this paper proposes a new method based on weighted networks, and the new method synthesizes and improves existent methods so that the predictor could make use of weight information in the network. We apply the new method to three real networks (astro-ph, cond-mat and hep-th). The result of experiment demonstrates that new method is more precise, and this method provides people with a new idea about how to better analyze weighted networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J Am. Soc. Inform. Sci. Technol. 58(7), 1019–1031 (2007)
Kumar, R., Novak, J., Tomkins, A.: Structure and evolution of online social networks. In: Proceedings of the ACM SIGKDD 2006, pp. 611–617. ACM Press, New York (2006)
Sarukkai, R.R.: Link prediction and path analysis using markov chains. Computer Networks 33(1-6), 377–386 (2000)
Zhu, J., Hong, J., Hughes, J.G.: Using Markov Chains for Link Prediction in Adaptive Web Sites. In: Bustard, D.W., Liu, W., Sterritt, R. (eds.) Soft-Ware 2002. LNCS, vol. 2311, pp. 60–73. Springer, Heidelberg (2002)
Popescul, A., Ungar, L.: Statistical relational learning for link prediction. In: Proceedings of the Workshop on Learning Statistical Models from Relational Data, pp. 81–87. ACM Press, New York (2003)
O’Madadhain, J., Hutchins, J., Smyth, P.: Prediction and ranking algorithms for event-based network data. In: Proceedings of the ACM SIGKDD 2005, pp. 23–30. ACM Press, New York (2005)
Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the 15th Intl. Conf. Mach. Learn., pp. 296–304. Morgan Kaufman Publishers, San Francisco (1998)
Clauset, A., Moore, C., Newman, M.E.J.: Hierarchical structure and the prediction of missing links in networks. Nature 453, 98–101 (2008)
Holland, P.W., Laskey, K.B., Leinhard, S.: Stochastic blockmodels: First steps. Social Networks 5, 109–137 (1983)
Getoor, L., Diehl, C.P.: Link mining: a survey. ACM SIGKDD Explorations Newsletter 7(2), 3–12 (2005)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Comput. Netw. & ISDN Syst. 30(1-7), 107–117 (1998)
Tong, H., Faloutsos, C., Pan, J.Y.: Fast random walk with restart and its applications. In: Proceedings of the 6th Intl. Conf. Data Min., pp. 613–622. IEEE Press, Washington, DC (2006)
Shang, M.S., Lü, L., Zeng, W., et al.: Relevance is more significant than correlation: Information filtering on sparse data. Europhys. Lett. 88(6), 68008 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, Z. et al. (2012). Link Prediction Based on Weighted Networks. In: Xiao, T., Zhang, L., Fei, M. (eds) AsiaSim 2012. AsiaSim 2012. Communications in Computer and Information Science, vol 324. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34390-2_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-34390-2_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34389-6
Online ISBN: 978-3-642-34390-2
eBook Packages: Computer ScienceComputer Science (R0)