Collaborative Research: CNS Core: Small: Closing the Theory-Practice Gap in Understanding and Combating Epidemic Spreading on Resource-Constrained Large-Scale Networks

联网设备的数量呈爆炸式增长。终端设备用户还建立了一堆丰富而复杂的网络，这些网络来源于他们的社会、个人和工作群体。然而，与终端设备和用户的大量连接可以被恶意软件和蠕虫攻击利用为破坏性的载体。由于利用网络连接是恶意软件传播的核心，因此了解底层网络结构如何影响恶意软件传播变得至关重要。尽管有大量关于流行病建模和分析的文献，但理论与实践之间仍然存在巨大差距。该项目旨在弥合差距，以便更好地了解和遏制流行病在具有现实成本限制的大规模网络上的传播。这个合作项目汇集了来自德克萨斯州立大学和北卡罗莱纳州立大学的研究人员，调查以下相互关联的研究重点。它将(1)建立一个理论框架，在一般图（而不是完整图）上充分表征流行病传播的瞬态动力学，以估计和预测未来每个节点被感染的可能性，(2)开发一套易于使用的算法，以在现实约束下尽可能地减轻流行病的传播。(3)开发了一套从不完整和有噪声的流行病级联数据中有效估计和推断网络和流行病参数的算法。该项目可能会对广泛的多学科领域和应用产生重大影响，其中流行病研究是必要和至关重要的，包括流行病学、物理和化学渗透、谣言传播、信息级联、病毒式营销以及错误信息和假新闻的传播。此外，该项目将通过课程开发将研究成果融入教育，让不同的本科生和研究生，特别是妇女和代表性不足群体的学生，参与其中，并培养他们在毕业后在工业和学术环境中茁壮成长并为社会做出贡献。在此项目过程中开发的所有产品将在项目结束后至少三年内公开提供并托管在https://sites.google.com/view/nsf-cns-eun-lee-epidemic上。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。

There has been an explosive growth in the number of Internet-connected devices. The end-device users have also built a stack of rich and complex networks, derived from their social, personal and work groups. The prolific connections to end-devices and users, however, can be exploited as devastating vehicles for malware and worm attacks. Since exploiting the network connectivity lies at the heart of malware distribution, it becomes crucial to understand how the underlying network structure affects the malware propagation. Despite abundant literature on epidemic modeling and analysis, there is still a huge gap between theory and practice. This project aims to bridge the gap to better understand and combat epidemic spreading on large-scale networks with realistic cost constraints. This collaborative project brings together investigators from Texas State University and North Carolina State University to investigate the following inter-related research thrusts. It will (1) develop a theoretical framework to fully characterize the transient dynamics of epidemic spreading on a general graph (as opposed to a complete graph) to estimate and predict the likelihood of each node being infected for the future time, (2) develop a suite of readily usable algorithms to mitigate the spread of an epidemic to the extent possible under realistic constraints, and (3) develop a set of algorithms for efficient estimation and inference of network and epidemic parameters from incomplete and noisy data of epidemic cascades. This project could potentially have a high impact on a vast range of multi-disciplinary areas and applications where the study of epidemics has been necessary and crucial, including epidemiology, percolation in physics and chemistry, rumor spreading, information cascades, viral marketing, and spread of misinformation and fake news. In addition, this project will integrate research findings into education by curriculum development, involve diverse undergraduate and graduate students, especially women and students of underrepresented groups, and have them trained to thrive and contribute to the society in industrial and academic settings after graduation. All products developed during the course of this project will be publicly available and hosted at https://sites.google.com/view/nsf-cns-eun-lee-epidemic for at least three years after the closing of the project. 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.