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10.1371/journal.pone.0150588 http://scihub22266oqcxt.onion/10.1371/journal.pone.0150588 C4782995!4782995!26954677     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26954677&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       PLoS+One 2016 ; 11 (3): ä Nephropedia Template TP {{tp|p=26954677|t=2016. Genealogical Trees of Scientific Papers |pdf=|usr=}}{{26954677}} gab.com Text Genealogical Trees of Scientific Papers JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=26954677 #FOAvip Many results have been obtained when studying scientific papers citations databases in a network perspective. Articles can be ranked according to their current in-degree and their future popularity or citation counts can even be predicted. The dynamical properties of such networks and the observation of the time evolution of their nodes started more recently. This work adopts an evolutionary perspective and proposes an original algorithm for the construction of genealogical trees of scientific papers on the basis of their citation count evolution in time. The fitness of a paper now amounts to its in-degree growing trend and a ?dying? paper will suddenly see this trend declining in time. It will give birth and be taken over by some of its most prevalent citing ?offspring?. Practically, this might be used to trace the successive published milestones of a research field. Twit Text FOAVip Genealogical Trees of Scientific Papers ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=26954677 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26954677 #FOAvip English Wikipedia <ref>{{Cite pmid|26954677}}</ref> Genealogical Trees of Scientific Papers #MMPMID26954677 Waumans MC; Bersini H PLoS One 2016[]; 11 (3): ä PMID26954677show ga Many results have been obtained when studying scientific papers citations databases in a network perspective. Articles can be ranked according to their current in-degree and their future popularity or citation counts can even be predicted. The dynamical properties of such networks and the observation of the time evolution of their nodes started more recently. This work adopts an evolutionary perspective and proposes an original algorithm for the construction of genealogical trees of scientific papers on the basis of their citation count evolution in time. The fitness of a paper now amounts to its in-degree growing trend and a ?dying? paper will suddenly see this trend declining in time. It will give birth and be taken over by some of its most prevalent citing ?offspring?. Practically, this might be used to trace the successive published milestones of a research field. äGenealogical Trees of Scientific Papers (epmc).pdf DeepDyve Pubget Overpricing