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

在线社交网络日益成为数十亿人获取实时新闻的主要来源。此外，在线社交网络通常充当发起和促进集体行动的媒介。该项目增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。与当前许多关注单一和孤立网络的文献不同，该项目考虑了在多个网络上发生的传染。通过结合数学建模和分析以及现实数据集的方法，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信息来应对这种情况。该项目将吸引来自弱势群体的学生，并将包括到高中的外展活动，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类主要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）和 ii）影响传播（即复杂传染）。 利用随机图论和渗流理论的高级工具来推导网络参数（例如度分布、大小、聚类、度相关性、耦合强度和传递性）与信息级联的阈值、概率和预期大小之间的基本关系。传染分析扩展到广泛的网络模型，包括现实世界的拓扑。复杂传染的建模从二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，该项目为分析由两个或多个随机图联合形成的随机图模型这一很大程度上尚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智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.