ATD: Algorithms for Threat Detection in Knowledge Graphs

ATD：知识图中的威胁检测算法

• 批准号: 2027277
• 负责人: Andrea Bertozzi
• 金额: $ 60.71万
• 依托单位: University of California-Los Angeles
• 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: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027277&HistoricalAwards=false
• 关键词: ATD; Algorithms; Threat; Detection; Knowledge

This project develops new mathematical algorithms and models involving knowledge graphs. A knowledge graph represents what is known about a subject in the form of labeled nodes and edges. More than simply labeled data, knowledge graphs organize data according to high-level meanings and assign globally unique identification to each node in the graph to match real-world entities. Much work on knowledge graphs treats databases and queries. In contrast, in the context of threat detection, this project focuses on algorithms that identify latent information in the graph and predictive models associated with data on the graph. The project will involve a combination of mathematical methods for subgraph isomorphism detection, time series analysis, agent-based and multiscale modeling, and pattern recognition. The project will train a postdoctoral scholar, PhD student, and six undergraduate researchers through involvement in the research.This project brings together several different focused problems with large, multimodal, complex datasets. The data is organized into a knowledge graph in which additional information is added and absorbed as it becomes available. This project considers three types of knowledge graphs each for different applications: (1) knowledge graphs constructed from complex multi-part narratives; (2) knowledge graphs constructed from heterogeneous online content; and (3) knowledge graphs associated with large-scale human interaction dynamics such as a global pandemic. For (1), algorithms will be designed to identify important causal subgraphs. For (2), the project aims to identify threats in space and time based on templated patterns. For (3), desired goals are both a predictive ability for actions from a micro to macro scale along with tools to assess potential impact versus cost of preventative measures, from local to regional to country-wide scale.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.

该项目开发了涉及知识图的新数学算法和模型。知识图以标记的节点和边的形式表示关于主题的已知内容。知识图不仅仅是简单的标记数据，它还根据高层含义组织数据，并为图中的每个节点分配全局唯一标识，以匹配真实世界的实体。关于知识图的许多工作涉及数据库和查询。相比之下，在威胁检测的背景下，该项目侧重于识别图中潜在信息的算法以及与图上数据相关的预测模型。该项目将涉及子图同构检测，时间序列分析，基于代理和多尺度建模和模式识别的数学方法的组合。该项目将通过参与研究培养一名博士后学者，博士生和六名本科生研究人员。该项目将几个不同的重点问题与大型，多模态，复杂的数据集结合在一起。数据被组织成一个知识图，在其中添加和吸收可用的额外信息。该项目考虑了三种不同应用的知识图：（1）由复杂的多部分叙述构建的知识图;（2）由异构在线内容构建的知识图;（3）与大规模人类互动动态（如全球流行病）相关的知识图。对于（1），将设计算法来识别重要的因果子图。对于（2），该项目旨在根据模板模式识别空间和时间上的威胁。对于（3），预期目标是从微观到宏观的行动预测能力沿着从地方到区域到全国范围的评估预防措施的潜在影响与成本的工具。该奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

Selectable Set Randomized Kaczmarz

• DOI: 10.1002/nla.2458
• 发表时间: 2021-10
• 期刊: Numerical Linear Algebra with Applications
• 影响因子: 4.3
• 作者: Yotam Yaniv;Jacob D. Moorman;W. Swartworth;Thomas K. Tu;Daji Landis;D. Needell

Knowledge Graphs of the QAnon Twitter Network

• DOI: 10.1109/bigdata55660.2022.10021128
• 发表时间: 2022-12
• 期刊: 2022 IEEE International Conference on Big Data (Big Data)
• 作者: Clay Adams;Malvina Bozhidarova;James Chen;Andrew Gao;Zhengtong Liu;J. Hunter Priniski;Junyuan Lin-Junyu

Subgraph Matching on Multiplex Networks

• DOI: 10.1109/tnse.2021.3056329
• 发表时间: 2021-02
• 期刊: IEEE Transactions on Network Science and Engineering
• 影响因子: 6.6
• 作者: Jacob D. Moorman;Thomas K. Tu;Qinyi Chen;Xie He;A. Bertozzi

Is the recent surge in violence in American cities due to contagion?

最近美国城市的暴力事件激增是因为蔓延吗？

• DOI: 10.1016/j.jcrimjus.2021.101848
• 发表时间: 2021
• 期刊: Journal of Criminal Justice
• 影响因子: 5.5
• 作者: Brantingham, P. Jeffrey;Carter, Jeremy;MacDonald, John;Melde, Chris;Mohler, George
• 通讯作者: Mohler, George

Project and Forget: Solving Large-Scale Metric Constrained Problems

• 发表时间: 2019-09
• 期刊: J. Mach. Learn. Res.
• 作者: A. Gilbert;Rishi Sonthalia