RnSIR模型的实现

Rajalakshmi E, Prajakta Kumbhojkar, Shyamsalonee Rawal, Masumi Jain, Sneha K. Thombre
{"title":"RnSIR模型的实现","authors":"Rajalakshmi E, Prajakta Kumbhojkar, Shyamsalonee Rawal, Masumi Jain, Sneha K. Thombre","doi":"10.1109/PUNECON.2018.8745392","DOIUrl":null,"url":null,"abstract":"By etymology, the word viral stems from virus, a term used to describe the spread of effect of infectious symptoms across organisms. On the internet, a piece of content can spread similar to a virus, making people infected as and when they come in contact with it. The infection usually occurs when the user shares it, with its circle of friends and associates on a social network. However, it is possible to predict the reach of information across a number of users in a directed network data set. This is possible through the proposed interface which uses the calculations proposed in the Restrained-Susceptible-Infected-Recovered (RnSIR) model. The interface accepts a data set as an input from the users whilst giving the percentage of information spread in that network as the output. The calculations at the interface back-end are done by using the same algorithms as used by the RnSIR model, to select influential nodes and then calculate the said percentage using them with the help of an algorithm. The interface poses to be useful for tracking the spread of information in a directed network for social media marketing and peripheral tactics.","PeriodicalId":166677,"journal":{"name":"2018 IEEE Punecon","volume":"128 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Implementation of the RnSIR Model\",\"authors\":\"Rajalakshmi E, Prajakta Kumbhojkar, Shyamsalonee Rawal, Masumi Jain, Sneha K. Thombre\",\"doi\":\"10.1109/PUNECON.2018.8745392\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"By etymology, the word viral stems from virus, a term used to describe the spread of effect of infectious symptoms across organisms. On the internet, a piece of content can spread similar to a virus, making people infected as and when they come in contact with it. The infection usually occurs when the user shares it, with its circle of friends and associates on a social network. However, it is possible to predict the reach of information across a number of users in a directed network data set. This is possible through the proposed interface which uses the calculations proposed in the Restrained-Susceptible-Infected-Recovered (RnSIR) model. The interface accepts a data set as an input from the users whilst giving the percentage of information spread in that network as the output. The calculations at the interface back-end are done by using the same algorithms as used by the RnSIR model, to select influential nodes and then calculate the said percentage using them with the help of an algorithm. The interface poses to be useful for tracking the spread of information in a directed network for social media marketing and peripheral tactics.\",\"PeriodicalId\":166677,\"journal\":{\"name\":\"2018 IEEE Punecon\",\"volume\":\"128 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE Punecon\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PUNECON.2018.8745392\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Punecon","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PUNECON.2018.8745392","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

从词源学上讲,“viral”一词源于“virus”,一个用来描述传染性症状在生物体之间传播的术语。在互联网上,一段内容可以像病毒一样传播,当人们接触到它时,就会被感染。感染通常发生在用户在社交网络上与朋友圈和同事分享这张照片的时候。然而,在有向网络数据集中,可以预测信息在多个用户之间的传播范围。这可以通过所建议的接口实现,该接口使用了在受限-易感-感染-恢复(RnSIR)模型中提出的计算。接口接受来自用户的数据集作为输入,同时给出在该网络中传播的信息的百分比作为输出。接口后端的计算使用与RnSIR模型相同的算法完成,选择有影响的节点,然后在算法的帮助下使用它们计算所述百分比。该界面可以用于跟踪信息在定向网络中的传播,用于社交媒体营销和外围策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation of the RnSIR Model
By etymology, the word viral stems from virus, a term used to describe the spread of effect of infectious symptoms across organisms. On the internet, a piece of content can spread similar to a virus, making people infected as and when they come in contact with it. The infection usually occurs when the user shares it, with its circle of friends and associates on a social network. However, it is possible to predict the reach of information across a number of users in a directed network data set. This is possible through the proposed interface which uses the calculations proposed in the Restrained-Susceptible-Infected-Recovered (RnSIR) model. The interface accepts a data set as an input from the users whilst giving the percentage of information spread in that network as the output. The calculations at the interface back-end are done by using the same algorithms as used by the RnSIR model, to select influential nodes and then calculate the said percentage using them with the help of an algorithm. The interface poses to be useful for tracking the spread of information in a directed network for social media marketing and peripheral tactics.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信