Link prediction in complex networks via matrix perturbation and decomposition
نویسندگان
چکیده
منابع مشابه
Link Prediction via Matrix Factorization
We propose to solve the link prediction problem in graphs using a supervised matrix factorization approach. The model learns latent features from the topological structure of a (possibly directed) graph, and is shown to make better predictions than popular unsupervised scores. We show how these latent features may be combined with optional explicit features for nodes or edges, which yields bett...
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Ratha Pech, Hao Dong1,2,∗, Liming Pan, Hong Cheng, Zhou Tao1,2,∗ 1 CompleX Lab, University of Electronic Science and Technology of China, Chengdu 611731, People’s Republic of China 2 Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, People’s Republic of China and 3 Center for Robotics, University of Electronic Science and Technology of China, Ch...
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Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives ...
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Nowadays, the link prediction problem in complex networks has attracted much attention. There are some difficulties in solving this problem, such as scarcity and huge size of networks. Most of the previous works have low efficiency. There are some solutions for this problem and we try to combine these solutions to find a better one. Our experiments in coauthorship networks show the truth of our...
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Many link prediction methods have been developed to infer unobserved links or predict latent links based on the observed network structure. However, due to network noises and irregular links in real network, the performances of existed methods are usually limited. Considering random noises and irregular links, we propose a perturbation-based framework based on Non-negative Matrix Factorization ...
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ژورنال
عنوان ژورنال: Scientific Reports
سال: 2017
ISSN: 2045-2322
DOI: 10.1038/s41598-017-14847-2