ATD: An Edge-Based PDE Paradigm and Inverse Analysis for Spatiotemporal Information Diffusion and Threat Detection

ATD：时空信息扩散和威胁检测的基于边缘的偏微分方程范式和逆分析

• 批准号: 2220373
• 负责人: Yang Yang
• 金额: $ 20万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2220373&HistoricalAwards=false
• 关键词: ATD; Edge; Based; PDE; Paradigm

The field of information diffusion aims to understand how information (such as news, diseases, and crises) spreads within social systems using large datasets. This type of research is crucial for comprehending human dynamics, detecting early-stage threats like infectious diseases or security risks, and proposing effective measures to monitor and control their spread. This project will establish a theoretical framework to characterize information propagation with spatially heterogeneous multiple-channel communications. By providing novel insights into the understanding of information propagation, this study will contribute to advancing the field of research, as well as the nation's health, prosperity, and welfare. The project will provide various educational activities to engage K-12, undergraduate, and graduate students pursuing STEM disciplines, particularly those from underrepresented groups with limited access to educational resources. These students will have the opportunity to receive comprehensive training in mathematical and programming skills, enabling them to become part of the next generation of STEM professionals. This project aims to develop a novel paradigm to model information diffusion for large spatiotemporal datasets using edge-based differential equations on metric graphs. The paradigm will take advantage of the two-step theory of information diffusion to form a metric graph of social groups, and then characterize information diffusion along distinct yet likely correlated channels using data-driven models. By combining the physics of information diffusion and mathematically-provable data-driven strategies, this approach achieves modeling that is both interpretable and computationally efficient. The research will also serve as a bridge between continuum differential equations and discrete differential equations, facilitating the translation of results between analysis and graph theory.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.

信息传播领域旨在了解信息（例如新闻，疾病和危机）如何使用大型数据集在社交系统中传播。这类研究对于理解人类动态，检测早期威胁（例如传染病或安全风险）以及提出有效的措施来监视和控制其传播至关重要。该项目将建立一个理论框架，以通过空间异质的多通道通信来表征信息传播。通过提供对信息传播理解的新颖见解，这项研究将有助于推进研究领域以及国家的健康，繁荣和福利。该项目将提供各种教育活动，以吸引K-12，本科生和研究生的研究生，尤其是来自代表性不足的群体的学科，获得教育资源的机会有限。这些学生将有机会接受数学和编程技能的全面培训，使他们能够成为下一代STEM专业人员的一部分。该项目旨在开发一种新型的范式，以使用指标图上的基于边缘的微分方程为大型时空数据集建模信息扩散。该范式将利用信息扩散的两步理论形成社会群体的度量图，然后使用数据驱动的模型来表征信息扩散沿不同但可能相关的通道。通过结合信息扩散的物理学和数学可证实的数据驱动策略，这种方法实现了既可以解释又有效率的建模。这项研究还将作为连续差分方程与离散差分方程之间的桥梁，促进了分析和图理论之间的结果的转换。该奖项反映了NSF的法定任务，并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。