恢复传播:谁负责扩散网络中的传染初始化?

Arman Sepehr, H. Beigy, Mohammadzaman Zamani, Shabnam Behzad
{"title":"恢复传播:谁负责扩散网络中的传染初始化?","authors":"Arman Sepehr, H. Beigy, Mohammadzaman Zamani, Shabnam Behzad","doi":"10.1109/IKT51791.2020.9345640","DOIUrl":null,"url":null,"abstract":"Millions of stories are transferred in a social network and some of them are malicious. Can we identify the source node(s) that are responsible to initiate the propagation originally? If so, when did they initiated the propagation? The problem of identifying the source of propagation based on limited observations has been studied significantly in recent years, as it can help to reduce the damage caused by unwanted infections with early detection. In this paper, we present an efficient algorithm for finding a node initiating a piece of information into the network and also inferring the time when it is initiated. We propose Source Location Estimation method, SoLE, that estimate the source probability for each node and then choose the source set which are maximize the probability using a well-known greedy method with a theoretical guarantees. The Observed nodes are detected nodes which are known clearly that spread specified malicious information in the network but small fraction of nodes are detected. The Hidden infected nodes are hidden, which spread the information in the network, however, they're not identified yet. In this problem, we first estimate the shortest path between other nodes to observed ones for each propagation trace, SoLE. Afterward, we find the best nodes as the source set among the hidden nodes by optimizing a loss function. Our experiments on real-world propagation through networks show the superiority of our approach in detecting true sources and promote the top ten accuracy from less than 10% for the state-of-the-art methods to approximately 30%.","PeriodicalId":382725,"journal":{"name":"2020 11th International Conference on Information and Knowledge Technology (IKT)","volume":"2009 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Revert Propagation: Who are responsible for a contagion initialization in a Diffusion Network?\",\"authors\":\"Arman Sepehr, H. Beigy, Mohammadzaman Zamani, Shabnam Behzad\",\"doi\":\"10.1109/IKT51791.2020.9345640\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Millions of stories are transferred in a social network and some of them are malicious. Can we identify the source node(s) that are responsible to initiate the propagation originally? If so, when did they initiated the propagation? The problem of identifying the source of propagation based on limited observations has been studied significantly in recent years, as it can help to reduce the damage caused by unwanted infections with early detection. In this paper, we present an efficient algorithm for finding a node initiating a piece of information into the network and also inferring the time when it is initiated. We propose Source Location Estimation method, SoLE, that estimate the source probability for each node and then choose the source set which are maximize the probability using a well-known greedy method with a theoretical guarantees. The Observed nodes are detected nodes which are known clearly that spread specified malicious information in the network but small fraction of nodes are detected. The Hidden infected nodes are hidden, which spread the information in the network, however, they're not identified yet. In this problem, we first estimate the shortest path between other nodes to observed ones for each propagation trace, SoLE. Afterward, we find the best nodes as the source set among the hidden nodes by optimizing a loss function. Our experiments on real-world propagation through networks show the superiority of our approach in detecting true sources and promote the top ten accuracy from less than 10% for the state-of-the-art methods to approximately 30%.\",\"PeriodicalId\":382725,\"journal\":{\"name\":\"2020 11th International Conference on Information and Knowledge Technology (IKT)\",\"volume\":\"2009 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 11th International Conference on Information and Knowledge Technology (IKT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IKT51791.2020.9345640\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 11th International Conference on Information and Knowledge Technology (IKT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IKT51791.2020.9345640","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

数以百万计的故事在社交网络上传播,其中一些是恶意的。我们能否确定最初负责发起传播的源节点?如果有,他们是什么时候开始传播的?近年来,基于有限的观察来确定传播源的问题已经得到了重要的研究,因为它可以帮助减少早期发现不必要的感染所造成的损害。在本文中,我们提出了一种有效的算法来寻找一个节点发起一条信息进入网络,并推断它被发起的时间。我们提出了源位置估计方法(Source Location Estimation method, SoLE),该方法估计每个节点的源概率,然后使用一种著名的贪婪方法选择概率最大的源集,并有理论上的保证。观察到的节点是已知在网络中传播特定恶意信息的被检测节点,但检测到的节点很少。隐藏的感染节点是隐藏的,它们在网络中传播信息,但尚未被识别。在这个问题中,我们首先估计每个传播路径中其他节点到观测节点之间的最短路径,即SoLE。然后,通过优化损失函数,在隐藏节点中找到最优节点作为源集。我们通过网络进行的真实世界传播实验表明,我们的方法在检测真实源方面具有优势,并将最先进方法的前十大准确率从不足10%提高到约30%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Revert Propagation: Who are responsible for a contagion initialization in a Diffusion Network?
Millions of stories are transferred in a social network and some of them are malicious. Can we identify the source node(s) that are responsible to initiate the propagation originally? If so, when did they initiated the propagation? The problem of identifying the source of propagation based on limited observations has been studied significantly in recent years, as it can help to reduce the damage caused by unwanted infections with early detection. In this paper, we present an efficient algorithm for finding a node initiating a piece of information into the network and also inferring the time when it is initiated. We propose Source Location Estimation method, SoLE, that estimate the source probability for each node and then choose the source set which are maximize the probability using a well-known greedy method with a theoretical guarantees. The Observed nodes are detected nodes which are known clearly that spread specified malicious information in the network but small fraction of nodes are detected. The Hidden infected nodes are hidden, which spread the information in the network, however, they're not identified yet. In this problem, we first estimate the shortest path between other nodes to observed ones for each propagation trace, SoLE. Afterward, we find the best nodes as the source set among the hidden nodes by optimizing a loss function. Our experiments on real-world propagation through networks show the superiority of our approach in detecting true sources and promote the top ten accuracy from less than 10% for the state-of-the-art methods to approximately 30%.
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
期刊最新文献
A New Sentence Ordering Method using BERT Pretrained Model Classical-Quantum Multiple Access Wiretap Channel with Common Message: One-Shot Rate Region Business Process Improvement Challenges: A Systematic Literature Review The risk prediction of heart disease by using neuro-fuzzy and improved GOA Distributed Learning Automata-Based Algorithm for Finding K-Clique in Complex Social Networks
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1