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• W1965664224 abstract "Low rank matrices approximations which have been used in networks link prediction are usually global optimal methods and use little local information. However, links are more likely to be found within dense blocks. It is also found that the block structure represents the local feature of matrices because entities in the same block have similar values. So we combines link prediction method by convex nonnegative matrix factorization with block detection to predict potential links using both of global and local information. A probabilistic latent variable model is presented by us and the experiments show that our method gives better prediction accuracy than original method alone (For example, AUC=0.861991 is higher 10% on Karate club network with 5% missing links.)." @default.
• W1965664224 created "2016-06-24" @default.
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• W1965664224 date "2014-08-01" @default.
• W1965664224 modified "2023-10-16" @default.
• W1965664224 title "Link prediction via nonnegative matrix factorization enhanced by blocks information" @default.
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• W1965664224 doi "https://doi.org/10.1109/icnc.2014.6975944" @default.
• W1965664224 hasPublicationYear "2014" @default.
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