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

合作研究：CNS核心：小型：缩小理解和抗击资源有限的大规模网络上的流行病传播的理论与实践差距

• 批准号: 2007828
• 负责人: Chul-Ho Lee
• 金额: $ 25万
• 依托单位: Florida Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2022-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007828&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Small

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 Florida Institute of Technology 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.

连接互联网的设备数量出现了爆炸性增长。终端设备用户还建立了一系列丰富而复杂的网络，这些网络来自他们的社交、个人和工作组。然而，与终端设备和用户的大量连接可能被用作恶意软件和蠕虫攻击的毁灭性工具。由于利用网络连接是恶意软件分发的核心，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，但理论和实践之间仍然存在着巨大的差距。该项目旨在弥合这一差距，以更好地了解和抗击在具有现实成本约束的大规模网络上传播的流行病。这个合作项目汇集了来自佛罗里达理工学院和北卡罗来纳州立大学的研究人员，以调查以下相互关联的研究推动力。它将(1)建立一个理论框架，在一般图(而不是完全图)上充分刻画流行病传播的瞬时动力学，以估计和预测未来时间每个节点被感染的可能性；(2)开发一套易于使用的算法，在现实约束下尽可能地缓解流行病的传播；(3)开发一套算法，用于从不完整和有噪声的流行病级联数据中有效地估计和推断网络和流行病参数。该项目可能对流行病研究十分必要和关键的广泛的多学科领域和应用领域产生重大影响，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整合到教育中，让不同的本科生和研究生参与进来，特别是女性和代表不足群体的学生，并培训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。在该项目过程中开发的所有产品将在项目结束后公开提供并托管至少三年。该奖项反映了https://sites.google.com/view/nsf-cns-eun-lee-epidemic的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Estimating Distributions of Large Graphs from Incomplete Sampled Data

• DOI: 10.23919/ifipnetworking52078.2021.9472848
• 发表时间: 2021-06
• 期刊: 2021 IFIP Networking Conference (IFIP Networking)
• 作者: Shiju Li;Xin Huang;Chul-Ho Lee

An Efficient and Scalable Algorithm for Estimating Kemeny's Constant of a Markov Chain on Large Graphs

• DOI: 10.1145/3447548.3467431
• 发表时间: 2021-08
• 期刊: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
• 作者: Shiju Li;Xin Huang;Chul-Ho Lee