SDSI: Source Detection in Structurally Incomplete Social Networks

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2024-12-31 DOI:10.1109/TNSE.2024.3522891

Le Cheng;Peican Zhu;Chao Gao;Zhen Wang;Xuelong Li

{"title":"SDSI: Source Detection in Structurally Incomplete Social Networks","authors":"Le Cheng;Peican Zhu;Chao Gao;Zhen Wang;Xuelong Li","doi":"10.1109/TNSE.2024.3522891","DOIUrl":null,"url":null,"abstract":"The dissemination of rumors imposes substantial hazards. Therefore, accurately identifying the source of rumor and promptly controlling information propagation hold paramount practical significance. Presently, prevailing sensor deployment methods solely focus on network structural information, disregarding the propagation process, thus incurring certain limitations. Additionally, source detection methods presuppose reliable assumptions, i.e., complete network structure and observational data. However, due to temporal constraints and cost considerations, the acquired network information is often structurally incomplete: partial edges missing. To address these issues, this paper introduces a novel approach, namely <bold>S</b>ource <bold>D</b>etection in <bold>S</b>tructurally <bold>I</b>ncomplete social networks (SDSI). Firstly, to monitor the network efficiently, a certain number of sensors are deployed using quality-guaranteed Monte Carlo simulations to achieve maximum coverage. In the source detection phase, considering the acquired incomplete information, the source node is determined based on Bayesian posterior maximum estimation. Additionally, SDSI is enhanced through incorporating the sharing counts of the information in social networks. Extensive experiments in diverse scenarios demonstrate the superiority and robustness of the proposed SDSI.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"1041-1052"},"PeriodicalIF":6.7000,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Network Science and Engineering","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10819254/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

Abstract

The dissemination of rumors imposes substantial hazards. Therefore, accurately identifying the source of rumor and promptly controlling information propagation hold paramount practical significance. Presently, prevailing sensor deployment methods solely focus on network structural information, disregarding the propagation process, thus incurring certain limitations. Additionally, source detection methods presuppose reliable assumptions, i.e., complete network structure and observational data. However, due to temporal constraints and cost considerations, the acquired network information is often structurally incomplete: partial edges missing. To address these issues, this paper introduces a novel approach, namely Source Detection in Structurally Incomplete social networks (SDSI). Firstly, to monitor the network efficiently, a certain number of sensors are deployed using quality-guaranteed Monte Carlo simulations to achieve maximum coverage. In the source detection phase, considering the acquired incomplete information, the source node is determined based on Bayesian posterior maximum estimation. Additionally, SDSI is enhanced through incorporating the sharing counts of the information in social networks. Extensive experiments in diverse scenarios demonstrate the superiority and robustness of the proposed SDSI.

查看原文本刊更多论文

谣言的传播具有很大的危害性。因此，准确识别谣言来源并及时控制信息传播具有重要的现实意义。目前，主流的传感器部署方法只关注网络结构信息，而忽略了传播过程，因此存在一定的局限性。此外，源检测方法的前提是可靠的假设，即完整的网络结构和观测数据。然而，由于时间限制和成本考虑，获取的网络信息往往结构不完整：部分边缘缺失。为了解决这些问题，本文提出了一种新方法，即结构不完整社交网络中的源检测（SDSI）。首先，为了有效地监控网络，利用质量保证蒙特卡洛模拟部署一定数量的传感器，以实现最大覆盖率。在源检测阶段，考虑到获取的不完整信息，基于贝叶斯后验最大估计确定源节点。此外，SDSI 还通过纳入社交网络中的信息共享计数而得到增强。在不同场景下进行的大量实验证明了所提出的 SDSI 的优越性和鲁棒性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.