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CIF: Small: Contagion Processes in Multi-layer and Multiplex Networks

CIF：小：多层和多重网络中的传染过程

基本信息

批准号：
1813637
负责人：
Osman Yagan
金额：
$ 50万
依托单位：
Carnegie-Mellon University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1813637&HistoricalAwards=false
关键词：
CIF Small Contagion Processes Multi

项目摘要

Online social networks are increasingly serving as a primary source of real time news for billions of people. In addition, online social networks often act as a medium to initiate and facilitate collective action. This project advances our understanding of contagion processes such as the spread of information and influence in online social networks. Unlike much of the current literature focusing on single and isolated networks, the project considers contagions taking place over multiple networks. By a combination of approaches involving mathematical modeling and analysis as well as real-life data sets, this project seeks a better understanding of i) how the participation of multiple networks affects the speed and extent that information propagates; ii) why different topics have different spreading characteristics over the same population; and iii) the ways misinformation spreads in social networks and how this might be countered by efficiently injecting correct information. The project will engage students from underrepresented groups and will include outreach activities to high-schools to encourage them to pursue a STEM education. This project seeks to advance the state-of-the art in modeling and analysis of two major classes of contagion processes over multi-layer and multiplex networks: i) Information propagation (i.e., simple contagion), and ii) Influence propagation (i.e., complex contagion). Advanced tools from random graph theory and percolation theory are leveraged to derive fundamental relations between network parameters (e.g., degree distribution, size, clustering, degree correlations, coupling strength, and transmissibility) and the threshold, probability, and expected size of information cascades. The analysis of contagion is extended to a wide range of network models including real-world topologies. The modeling of complex contagion is extended from binary-state dynamics to multi-stage dynamics and this will be utilized in studying the spread of misinformation. On a broader level, this project contributes to a largely unexplored field of analyzing random graph models formed by the union of two or more random graphs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在线社交网络正日益成为数十亿人获取真实的实时新闻的主要来源。此外，在线社交网络往往是发起和促进集体行动的媒介。该项目推进了我们对传染过程的理解，例如在线社交网络中的信息传播和影响力。与目前大多数关注单个和孤立网络的文献不同，该项目认为传染发生在多个网络上。该项目综合运用数学建模和分析以及现实生活数据集等方法，力求更好地了解：（一）多个网络的参与如何影响信息传播的速度和范围;（二）为什么不同的主题在同一人群中具有不同的传播特点;以及iii）错误信息在社交网络中传播的方式，以及如何通过有效地注入正确信息来应对。该项目将吸引来自代表性不足群体的学生，并将包括对高中的外联活动，以鼓励他们接受STEM教育。该项目旨在推进多层和多重网络上两大类传染过程的建模和分析的最新技术：i）信息传播（即，简单传染），以及ii）影响传播（即，复杂传染病）。利用来自随机图论和渗流理论的高级工具来导出网络参数之间的基本关系（例如，度分布、大小、聚类、度相关性、耦合强度和可传递性）以及信息级联的阈值、概率和预期大小。蔓延的分析扩展到广泛的网络模型，包括现实世界的拓扑结构。将复杂传染模型从二态动力学扩展到多阶段动力学，并将其用于研究错误信息的传播。在更广泛的层面上，该项目为分析由两个或多个随机图联合形成的随机图模型这一基本上未探索的领域做出了贡献。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值进行评估而被认为值得支持。和更广泛的影响审查标准。