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10.1007/s10844-020-00623-8 http://scihub22266oqcxt.onion/10.1007/s10844-020-00623-8 33071464!7548310!33071464     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 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Intell+Inf+Syst 2021 ; 56 (2): 355-377 Nephropedia Template TP {{tp|p=33071464|t=2021. Modeling information diffusion in online social networks using a modified forest-fire model |pdf=|usr=}}{{33071464}} gab.com Text Modeling information diffusion in online social networks using a modified forest-fire model JO: J Intell Inf Syst http://www.kidney.de/pget.php?&tw=1&pmid=33071464 #FOAvip Information dissemination has changed rapidly in recent years with the emergence of social media which provides online platforms for people worldwide to share their thoughts, activities, emotions, and build social relationships. Hence, modeling information diffusion has become an important area of research in the field of network analysis. It involves the mathematical modeling of the movement of information and study the information spread pattern. In this paper, we attempt to model information propagation in online social networks using a nature-inspired approach based on a modified forest-fire model. A slight spark can start a wildfire in a forest, and the spread of this fire depends on vegetation, weather, and topography, which may act as fuel. On similar lines, we labeled users who haven't joined the network yet as E m p t y, existing users as T r e e, and information as F i r e. The spread of information across online social networks depends upon users-followers relationships, the significance of the topic, and other such features. We introduce a novel B u r n t state to the traditional forest-fire model to represent non-spreaders in the network. We validate our method on six real-world data-sets extracted from Twitter and conclude that the proposed model performs reasonably well in predicting information diffusion. Twit Text FOAVip Modeling information diffusion in online social networks using a modified forest-fire model ????J Intell Inf Syst http://www.kidney.de/pget.php?&tw=1&pmid=33071464 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33071464 #FOAvip English Wikipedia <ref>{{Cite pmid|33071464}}</ref> Modeling information diffusion in online social networks using a modified forest-fire model #MMPMID33071464 Kumar S; Saini M; Goel M; Panda BS J Intell Inf Syst 2021[]; 56 (2): 355-377 PMID33071464show ga Information dissemination has changed rapidly in recent years with the emergence of social media which provides online platforms for people worldwide to share their thoughts, activities, emotions, and build social relationships. Hence, modeling information diffusion has become an important area of research in the field of network analysis. It involves the mathematical modeling of the movement of information and study the information spread pattern. In this paper, we attempt to model information propagation in online social networks using a nature-inspired approach based on a modified forest-fire model. A slight spark can start a wildfire in a forest, and the spread of this fire depends on vegetation, weather, and topography, which may act as fuel. On similar lines, we labeled users who haven't joined the network yet as E m p t y, existing users as T r e e, and information as F i r e. The spread of information across online social networks depends upon users-followers relationships, the significance of the topic, and other such features. We introduce a novel B u r n t state to the traditional forest-fire model to represent non-spreaders in the network. We validate our method on six real-world data-sets extracted from Twitter and conclude that the proposed model performs reasonably well in predicting information diffusion. äModeling information diffusion in online social networks using a modif(epmc).pdf DeepDyve Pubget Overpricing