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10.1371/journal.pone.0193331 http://scihub22266oqcxt.onion/10.1371/journal.pone.0193331 C5825074!5825074!29474409     free     free     free       PLoS+One 2018 ; 13 (2): ä Nephropedia Template TP {{tp|p=29474409|t=2018. A generative model for scientific concept hierarchies |pdf=|usr=}}{{29474409}} gab.com Text A generative model for scientific concept hierarchies JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=29474409 #FOAvip In many scientific disciplines, each new ?product? of research (method, finding, artifact, etc.) is often built upon previous findings?leading to extension and branching of scientific concepts over time. We aim to understand the evolution of scientific concepts by placing them in phylogenetic hierarchies where scientific keyphrases from a large, longitudinal academic corpora are used as a proxy of scientific concepts. These hierarchies exhibit various important properties, including power-law degree distribution, power-law component size distribution, existence of a giant component and less probability of extending an older concept. We present a generative model based on preferential attachment to simulate the graphical and temporal properties of these hierarchies which helps us understand the underlying process behind scientific concept evolution and may be useful in simulating and predicting scientific evolution. Twit Text FOAVip A generative model for scientific concept hierarchies ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=29474409 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29474409 #FOAvip English Wikipedia <ref>{{Cite pmid|29474409}}</ref> A generative model for scientific concept hierarchies #MMPMID29474409 Datta S; Adar E PLoS One 2018[]; 13 (2): ä PMID29474409show ga In many scientific disciplines, each new ?product? of research (method, finding, artifact, etc.) is often built upon previous findings?leading to extension and branching of scientific concepts over time. We aim to understand the evolution of scientific concepts by placing them in phylogenetic hierarchies where scientific keyphrases from a large, longitudinal academic corpora are used as a proxy of scientific concepts. These hierarchies exhibit various important properties, including power-law degree distribution, power-law component size distribution, existence of a giant component and less probability of extending an older concept. We present a generative model based on preferential attachment to simulate the graphical and temporal properties of these hierarchies which helps us understand the underlying process behind scientific concept evolution and may be useful in simulating and predicting scientific evolution. äA generative model for scientific concept hierarchies (epmc).pdf DeepDyve Pubget Overpricing