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

与安全和威胁检测相关的数据库通常具有复杂的多模态结构，其中包含地理信息、时间戳、名称标签、人类行为模式等链接在一起。组织这类数据的一种现代方法是以知识图的形式。如此庞大的信息结构可能包含隐藏信息。该项目将专注于检测大型复杂数据结构中的活动模板，这些数据结构被组织成一个多路图。现实世界数据的例子包括交通网络、地理标记Twitter数据的知识图和模拟人类活动的合成图。该项目旨在研究具有大且组合复杂解空间的子图匹配问题。为了浏览可能与已知活动模板具有相似模式的无数信息，可能需要主题专家提供额外的信息。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。研究将解决精确和不精确子图匹配。子图匹配的度量将包括图拓扑测量（例如，图编辑距离）、时间戳比较、标签属性的相似性（例如，Levenshtein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。

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.