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10.1371/journal.pone.0193703 http://scihub22266oqcxt.onion/10.1371/journal.pone.0193703 C5863954!5863954!29566100     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2018 ; 13 (3): ä Nephropedia Template TP {{tp|p=29566100|t=2018. Authorship attribution based on Life-Like Network Automata |pdf=|usr=}}{{29566100}} gab.com Text Authorship attribution based on Life-Like Network Automata JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=29566100 #FOAvip The authorship attribution is a problem of considerable practical and technical interest. Several methods have been designed to infer the authorship of disputed documents in multiple contexts. While traditional statistical methods based solely on word counts and related measurements have provided a simple, yet effective solution in particular cases; they are prone to manipulation. Recently, texts have been successfully modeled as networks, where words are represented by nodes linked according to textual similarity measurements. Such models are useful to identify informative topological patterns for the authorship recognition task. However, there is no consensus on which measurements should be used. Thus, we proposed a novel method to characterize text networks, by considering both topological and dynamical aspects of networks. Using concepts and methods from cellular automata theory, we devised a strategy to grasp informative spatio-temporal patterns from this model. Our experiments revealed an outperformance over structural analysis relying only on topological measurements, such as clustering coefficient, betweenness and shortest paths. The optimized results obtained here pave the way for a better characterization of textual networks. Twit Text FOAVip Authorship attribution based on Life-Like Network Automata ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=29566100 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29566100 #FOAvip English Wikipedia <ref>{{Cite pmid|29566100}}</ref> Authorship attribution based on Life-Like Network Automata #MMPMID29566100 Machicao J; Corrêa EA; Miranda GHB; Amancio DR; Bruno OM PLoS One 2018[]; 13 (3): ä PMID29566100show ga The authorship attribution is a problem of considerable practical and technical interest. Several methods have been designed to infer the authorship of disputed documents in multiple contexts. While traditional statistical methods based solely on word counts and related measurements have provided a simple, yet effective solution in particular cases; they are prone to manipulation. Recently, texts have been successfully modeled as networks, where words are represented by nodes linked according to textual similarity measurements. Such models are useful to identify informative topological patterns for the authorship recognition task. However, there is no consensus on which measurements should be used. Thus, we proposed a novel method to characterize text networks, by considering both topological and dynamical aspects of networks. Using concepts and methods from cellular automata theory, we devised a strategy to grasp informative spatio-temporal patterns from this model. Our experiments revealed an outperformance over structural analysis relying only on topological measurements, such as clustering coefficient, betweenness and shortest paths. The optimized results obtained here pave the way for a better characterization of textual networks. äAuthorship attribution based on Life-Like Network Automata (epmc).pdf DeepDyve Pubget Overpricing