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

在线社交网络正日益成为数十亿人获取实时新闻的主要来源。此外，在线社交网络经常充当发起和促进集体行动的媒介。这个项目促进了我们对传染过程的理解，比如在线社交网络中的信息传播和影响。与目前大多数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中发生的传染。通过结合数学建模和分析以及真实数据集的方法，该项目寻求更好地理解：(1)多个网络的参与如何影响信息传播的速度和范围；Ii)为什么不同的话题在相同的人群中有不同的传播特征；第三，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的信息来应对。该项目将吸引来自代表性不足群体的学生，并将包括到高中的外展活动，以鼓励他们接受STEM教育。本项目旨在推动对多层和多路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）。利用随机图理论和渗透理论的先进工具来推导网络参数（例如，度分布、大小、聚类、度相关性、耦合强度和传递率）与信息级联的阈值、概率和预期大小之间的基本关系。传染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，这将用于研究错误信息的传播。在更广泛的层面上，该项目有助于分析两个或多个随机图的并集形成的随机图模型，这是一个很大程度上尚未开发的领域。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识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.