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2014 Link Prediction via Convex Nonnegative Matrix Factorization on Multiscale Blocks
Enming Dong, Jianping Li, Zheng Xie
J. Appl. Math. 2014: 1-9 (2014). DOI: 10.1155/2014/786156


Low rank matrices approximations have been used in link prediction for networks, which are usually global optimal methods and lack of using the local information. The block structure is a significant local feature of matrices: entities in the same block have similar values, which implies that links are more likely to be found within dense blocks. We use this insight to give a probabilistic latent variable model for finding missing links by convex nonnegative matrix factorization with block detection. The experiments show that this method gives better prediction accuracy than original method alone. Different from the original low rank matrices approximations methods for link prediction, the sparseness of solutions is in accord with the sparse property for most real complex networks. Scaling to massive size network, we use the block information mapping matrices onto distributed architectures and give a divide-and-conquer prediction method. The experiments show that it gives better results than common neighbors method when the networks have a large number of missing links.


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Enming Dong. Jianping Li. Zheng Xie. "Link Prediction via Convex Nonnegative Matrix Factorization on Multiscale Blocks." J. Appl. Math. 2014 1 - 9, 2014.


Published: 2014
First available in Project Euclid: 2 March 2015

zbMATH: 07131861
MathSciNet: MR3240636
Digital Object Identifier: 10.1155/2014/786156

Rights: Copyright © 2014 Hindawi


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