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10.1038/srep22955 http://scihub22266oqcxt.onion/10.1038/srep22955 C4785364!4785364!26961965     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2016 ; 6 (ä): ä Nephropedia Template TP {{tp|p=26961965|t=2016. Predicting missing links and identifying spurious links via likelihood analysis |pdf=|usr=}}{{26961965}} gab.com Text Predicting missing links and identifying spurious links via likelihood analysis JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=26961965 #FOAvip Real network data is often incomplete and noisy, where link prediction algorithms and spurious link identification algorithms can be applied. Thus far, it lacks a general method to transform network organizing mechanisms to link prediction algorithms. Here we use an algorithmic framework where a network?s probability is calculated according to a predefined structural Hamiltonian that takes into account the network organizing principles, and a non-observed link is scored by the conditional probability of adding the link to the observed network. Extensive numerical simulations show that the proposed algorithm has remarkably higher accuracy than the state-of-the-art methods in uncovering missing links and identifying spurious links in many complex biological and social networks. Such method also finds applications in exploring the underlying network evolutionary mechanisms. Twit Text FOAVip Predicting missing links and identifying spurious links via likelihood analysis ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=26961965 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26961965 #FOAvip English Wikipedia <ref>{{Cite pmid|26961965}}</ref> Predicting missing links and identifying spurious links via likelihood analysis #MMPMID26961965 Pan L; Zhou T; Lü L; Hu CK Sci Rep 2016[]; 6 (ä): ä PMID26961965show ga Real network data is often incomplete and noisy, where link prediction algorithms and spurious link identification algorithms can be applied. Thus far, it lacks a general method to transform network organizing mechanisms to link prediction algorithms. Here we use an algorithmic framework where a network?s probability is calculated according to a predefined structural Hamiltonian that takes into account the network organizing principles, and a non-observed link is scored by the conditional probability of adding the link to the observed network. Extensive numerical simulations show that the proposed algorithm has remarkably higher accuracy than the state-of-the-art methods in uncovering missing links and identifying spurious links in many complex biological and social networks. Such method also finds applications in exploring the underlying network evolutionary mechanisms. äPredicting missing links and identifying spurious links via likelihood(epmc).pdf DeepDyve Pubget Overpricing