Abstract
Link prediction is an important issue to understand the dynamics and evolution mechanisms of complex networks. Traditional link prediction algorithms are based on the topological properties of the underlying network in terms of graph theory. In order to improve the accuracy of link prediction, recent researches increasingly focus on modeling the link behaviors from the latent structure information of the networks. In this paper, we propose a neighborhood-based nonnegative matrix factorization model to solve the problem of link prediction. Our model learns latent feature factors from the overall topological structure combing with local neighborhood structures of the underlying network. Extensive experiments on real-world networks demonstrate the effectiveness and efficiency of our proposed model.
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Acknowledgements
This work is funded by National Science Foundation of China (61271316 and 61071152), 973 Program (2010CB731403, 2010CB731406 and 2013CB329605), Chinese National “Twelfth Five-Year” Plan for Science & Technology Support (2012BAH38 B04), Key Laboratory for Shanghai Integrated Information Security Management Technology Research and Chinese National Engineering Laboratory for Information Content Analysis Technology.
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Zhao, Y., Li, S., Zhao, C., Jiang, W. (2015). Link Prediction via a Neighborhood-Based Nonnegative Matrix Factorization Model. In: Mu, J., Liang, Q., Wang, W., Zhang, B., Pi, Y. (eds) The Proceedings of the Third International Conference on Communications, Signal Processing, and Systems. Lecture Notes in Electrical Engineering, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-319-08991-1_62
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DOI: https://doi.org/10.1007/978-3-319-08991-1_62
Publisher Name: Springer, Cham
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