Abstract
Link prediction has attracted attention from multiple research areas. Although several – mostly unsupervised – link prediction methods have been proposed, improving them is still under study. In several fields of science, noise is used as an advantage to improve information processing, inspiring us to also investigate noise enhancement in link prediction. In this research, we study link prediction from a data preprocessing point of view by introducing a noise-enhanced link prediction framework that improves the links predicted by current link prediction heuristics. The framework proposes three noise methods to help predict better links. Theoretical explanation and extensive experiments on synthetic and real-world datasets show that our framework helps improve current link prediction methods.
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References
Abdolazimi, R., Jin, S., Zafarani, R.: Noise-enhanced community detection. In: Proceedings of the 31st ACM Conference on Hypertext and Social Media, pp. 271–280 (2020)
Audhkhasi, K., Osoba, O., Kosko, B.: Noise-enhanced convolutional neural networks. Neural Netw. 78, 15–23 (2016)
Chen, H., Varshney, L.R., Varshney, P.K.: Noise-enhanced information systems. PIEEE (2014)
Gammaitoni, L., Hänggi, P., Jung, P., Marchesoni, F.: Stochastic resonance. Rev. Modern Phys 70(1), 223 (1998)
Kay, S.: Can detectability be improved by adding noise? IEEE Signal Proc. Lett. 7(1), 8–10 (2000)
Krishna, O., Jha, R.K., Tiwari, A.K., Soni, B.: Noise induced segmentation of noisy color image. In: 2013 NCC, pp. 1–5, February 2013
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inform. Sci. Technol. 58(7), 1019–1031 (2007)
Lichtnwalter, R., Chawla, N.V.: Link prediction: fair and effective evaluation. In: 2012 IEEE/ACM ASONAM, pp. 376–383. IEEE (2012)
Lü, L., Zhou, T.: Link prediction in complex networks: a survey. Phys. A 390(6), 1150–1170 (2011)
McDonnell, M.D., Abbott, D.: What is stochastic resonance? definitions, misconceptions, debates, and its relevance to biology. PLoS Comp. Bio. 5(5) (2009)
McDonnell, M.D., Ward, L.M.: The benefits of noise in neural systems: bridging theory and experiment. Nat. Rev. Neurosci. 12(7), 415 (2011)
Mislove, A.: Online social networks: measurement, analysis, and applications to distributed information systems. Ph.D. thesis, Rice University, Department of Computer Science, May 2009
Newman, M.E.: The structure and function of complex networks. SIAM Rev. 45(2), 167–256 (2003)
Opsahl, T., Panzarasa, P.: Clustering in weighted networks. Soc. Netw. 31(2), 155–163 (2009)
Osoba, O., Kosko, B.: Noise-enhanced clustering and competitive learning algorithms. Neural Netw. 37, 132–140 (2013)
Osoba, O., Mitaim, S., Kosko, B.: The noisy expectation-maximization algorithm. Fluct. Noise Lett. 12(03), 1350012 (2013)
Simonotto, E., Riani, M., Seife, C., Roberts, M., Twitty, J., Moss, F.: Visual perception of stochastic resonance. Phys. Rev. Lett. 78 (1997)
Tang, K.S., Man, K.F., Kwong, S., He, Q.: Genetic algorithms and their applications. IEEE Signal Process. Mag. 13(6), 22–37 (1996)
Viswanath, B., Mislove, A., Cha, M., Gummadi, K.P.: On the evolution of user interaction in Facebook. In: Proceedings of the 2nd ACM SIGCOMM Workshop on Social Networks (WOSN 2009), August 2009
Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440 (1998)
Zozor, S., Amblard, P.O.: On the use of stochastic resonance in sine detection. Signal Proc. 82(3) (2002)
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Abdolazimi, R., Zafarani, R. (2021). Noise-Enhanced Unsupervised Link Prediction. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12712. Springer, Cham. https://doi.org/10.1007/978-3-030-75762-5_38
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DOI: https://doi.org/10.1007/978-3-030-75762-5_38
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