ATD: Active Learning Activity Detection in Multiplex Networks of Geospatial-Cyber-Temporal Data

ATD：地理空间网络时空数据多重网络中的主动学习活动检测

• 批准号: 2318817
• 负责人: Andrea Bertozzi
• 金额: $ 10万
• 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: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2318817&HistoricalAwards=false
• 关键词: ATD; Active; Learning; Activity; Detection

Databases of relevance to security and threat detection often have complex multimodal structures with geographic information, timestamps, name labels, human behavior patterns, and more linked together. A modern way of organizing such data is in the form of knowledge graphs. Such large information structures can have hidden information. This project will focus on detecting templates of activity in large and complex data structures organized as a multiplex graph. Examples of real-world data include transportation networks, knowledge graphs of geotagged Twitter data, and synthetic graphs that model human activity. The project aims to investigate a subgraph matching problem that can have a large and combinatorially complex solution space. In order to wade through the myriad of information that might have similar patterns to a known activity template, subject matter experts may be called on to provide additional information. This project will develop algorithms that incorporate a human in the loop and are thus called active learning methods. The research will address both exact and inexact subgraph matching. Metrics for subgraph matching will include graph topology measurements (e.g., graph edit distance), timestamp comparisons, similarities in label attributes (e.g., Levenshtein distance), and geographic distances. The project will quantify both template and world graph active learning strategies.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.

与安全和威胁检测相关的数据库通常具有复杂的多模式结构，其中包含地理信息、时间戳、名称标签、人类行为模式等。组织此类数据的现代方法是知识图的形式。如此大的信息结构可能包含隐藏信息。该项目将重点检测以多重图形式组织的大型复杂数据结构中的活动模板。现实世界数据的示例包括交通网络、地理标记 Twitter 数据的知识图以及模拟人类活动的合成图。该项目旨在研究可能具有较大且组合复杂的解决方案空间的子图匹配问题。为了费力地浏览可能与已知活动模板具有相似模式的大量信息，可能会要求主题专家提供附加信息。该项目将开发将人类纳入循环的算法，因此被称为主动学习方法。该研究将解决精确和不精确的子图匹配问题。子图匹配的指标将包括图拓扑测量（例如图编辑距离）、时间戳比较、标签属性的相似性（例如编辑距离）和地理距离。该项目将量化模板和世界图主动学习策略。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值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

AutoKG: Efficient Automated Knowledge Graph Generation for Language Models

• DOI: 10.1109/bigdata59044.2023.10386454
• 发表时间: 2023-11
• 期刊: 2023 IEEE International Conference on Big Data (BigData)
• 作者: Bohan Chen;Andrea L. Bertozzi

Hate speech and hate crimes: a data-driven study of evolving discourse around marginalized groups

仇恨言论和仇恨犯罪：关于边缘群体话语演变的数据驱动研究

• DOI: 10.1109/bigdata59044.2023.10386312
• 发表时间: 2023
• 期刊: Proc. 2023 IEEE International Conference on Big Data (BigData
• 作者: Bozhidarova, Malvina;Chang, Jonathn;Ale-Rasool, Aaishah;Liu, Yuxiang;Ma, Chongyao;Bertozzi, Andrea L.;Brantingham, P. Jeffrey;Lin, Junyuan;Krishnagopal, Sanjukta
• 通讯作者: Krishnagopal, Sanjukta