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

• 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; CIF; Small; Contagion; Processes; Multi

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.

在线社交网络越来越多地成为数十亿人的实时新闻的主要来源。此外，在线社交网络通常是发起和促进集体行动的媒介。该项目提高了我们对传染过程的理解，例如信息和影响力在在线社交网络中的传播。与当前关注单个网络和孤立网络的许多文献不同，该项目考虑了在多个网络上发生的传染。通过涉及数学建模和分析以及现实生活数据集的方法的结合，该项目寻求更好地理解i）多个网络的参与如何影响信息传播的速度和程度； ii）为什么不同的主题在同一人群中具有不同的传播特征； iii）错误信息在社交网络中传播的方式以及如何通过有效注入正确的信息来反驳这一问题。该项目将吸引来自代表性不足的团体的学生，并将包括向高中生的外展活动，以鼓励他们接受STEM教育。该项目旨在推进对多层和多重网络的两种主要传染过程建模和分析的最新技术：i）信息传播（即简单传播），ii）影响传播（即复杂的传播）。 从随机图理论和渗透理论中的先进工具被利用为获得网络参数之间的基本关系（例如，程度分布，大小，聚类，度量相关性，耦合强度和传播性）以及信息级联的阈值，概率和预期的大小。传播的分析扩展到包括现实世界拓扑在内的广泛网络模型。复杂传播的建模从二进制态动力学扩展到多阶段动力学，这将用于研究错误信息的传播。在更广泛的层面上，该项目在很大程度上有助于一个未开发的领域，该领域是由两个或多个随机图的结合形成的随机图模型。该奖项反映了NSF的法定任务，并且认为值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来获得支持。