ATD: Models of Spreading Dynamics in Multilayer Networks

民意调查将进行一项为期三年的项目，研究意见和内容在多渠道通信网络中的传播。许多人依赖网络获取新闻和信息；人们在这些相互关联的媒体上传播不同的内容。然而，媒体上的内容在质量和可信度上各不相同，从高质量和彻底研究的内容到吹捧和错误信息。PI将把在线内容的质量和观点纳入观点和内容传播的新颖数学模型，它们对在线讨论经济和社会政策等问题以及与持不同观点的人的互动产生巨大影响。本项目将研究多通道通信网络的结构；分析这些网络上的新数学模型，以深入了解这些网络上的内容传播与用户意见转变相互作用的机制；并利用这种分析来制定策略，以类似于垃圾邮件过滤器的方式标记内容，从而帮助引导用户更好地浏览各种形式的媒体内容。该项目的结果也将直接告知并反馈到许多学科的网络理论和应用中，例如通过改进研究网络结构如何影响网络上发生的过程（内容传播、运输、营养物质流动等）的方法。该项目每年将资助2名研究生。民意调查将进行一项为期三年的项目，研究意见和内容在多渠道通信网络中的传播。这个项目有三个组成部分。首先，利用多层网络的新数学形式，PI将分析多通道通信网络的结构，并以多层随机图的形式建立其网络模型。其次，PI将意见动态的有界置信度模型从单层网络推广到多层网络（这种类型的动态过程以前没有在多层网络上研究过，可以使用多层网络编码具有多种边缘类型或由多个子系统组成的网络），PI将通过蒙特卡罗模拟和回归分析系统地分析这些模型的行为。第三，PI将开发一个意见（连续值的，可以在一个或多个维度上）和内容（通过离散选择传播）相互传播的模型，通过增加多层有界置信度模型与传播异构内容质量信息的媒体节点。使用该模型，PI将开发方法来检查低质量和高质量内容的传播特性之间的结构差异，从而提高过滤网络中不同形式内容的能力。该项目的结果还将直接告知和反馈到许多学科的网络理论和应用中，例如通过提供确定多层网络中层间边权重的原则方法（多层网络分析应用中的一个关键开放问题）。多层意见动力学的新模型也将更广泛地影响网络动态过程的研究，推进对多层网络架构产生的新相变和分岔的研究。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。

The PI will undertake a three-year project to study opinion and content spreading in multichannel communication networks. Many people rely on online sources for their news and information; and people spread diverse content on these interconnected media. However, the content on media varies in quality and trustworthiness, from high-quality and thoroughly-researched content to puff pieces and misinformation. Both the quality and the viewpoint of online content, which the PI will incorporate into novel mathematical models of opinion and content spreading, have an enormous influence on online discussions on issues such as economic and social policy and on interactions with others of differing views. This project will study the structure of multichannel communication networks; analyze new mathematical models on these networks to gain a thorough understanding of the mechanisms of content spread interacting with opinion shifts of users on these networks; and use this analysis to develop strategies to flag content in a way that is analogous to spam filters, thereby helping guide users to better navigate content on various forms of media. The results of this project will also inform and feed back directly into theory and applications of networks in many disciplines, such as by improving methodology for studying how the structure of networks influences processes (content spreading, transportation, flow of nutrients, and others) that occur on networks. This project will support 2 graduate students each year.The PI will undertake a three-year project to study opinion and content spreading in multichannel communication networks. This project has three components. First, using the new mathematical formalism of multilayer networks, the PI will analyze the structure of multichannel communication networks and develop network models of it in the form of multilayer random graphs. Second, the PI will generalize bounded-confidence models of opinion dynamics from monolayer networks to multilayer networks (this type of dynamical process has not been studied previously on multilayer networks, which one can use to encode networks with multiple types of edges or which consist of multiple subsystems), and the PI will systematically analyze the behavior of the these models through Monte Carlo simulations and regression analysis. Third, the PI will develop a model of interacting spreading of opinions (which are continuous-valued and can be in one or more dimensions) and content (which spreads through a discrete choice) by augmenting multilayer bounded-confidence models with media nodes that spread information of heterogeneous content qualities. Using this model, the PI will develop methodology to examine structural differences between the spreading properties of low-quality and high-quality content, thereby advancing the ability to filter disparate forms of content in networks. The results of this project will also inform and feed back directly into theory and applications of networks in many disciplines, such as by providing a principled method for determining the weights of interlayer edge in multilayer networks (a key open issue in applications of multilayer network analysis). The novel model of multilayer opinion dynamics will also influence the study of dynamical processes on networks more generally, advancing studies on novel phase transitions and bifurcations that arise from multilayer network architectures.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.