EAGER: Collaborative Research: Substructure-aware Spatiotemporal Representation Learning

时空网络化数据是交通网络、电网和社会网络等关键基础设施信息的重要表征。交通网络中不断发展的车辆移动性可以定位交通堵塞的根源。社交网络的动态转发关键词可能会通知一种新的疾病爆发。该项目将开发新技术，使机器具备自动化和精确的时空网络表征。这个项目的主要新颖之处在于它能够保存表征时空网络数据的子结构模式。通过识别和表征这些子结构模式，如交通堵塞的子网络，过载或中断的子网络，计算机可以更好地提取语义，预测趋势并检测异常，这对关键基础设施的运营，管理和防御非常重要。在运输操作中，开发的技术有可能改变土木工程师如何识别碰撞、死亡和事故前兆的行为因素和周围特征。对于电网管理的研究人员来说，自动化和精确的表征方法可以帮助了解发电损耗、大负荷、串联电容器故障和线路跳闸等突发事件的对策和特征。在公共卫生和流行病方面，子结构意识将能够从社会网络中微妙的自然语言模式中快速、早期地发现新疾病。该项目将开发新的机器学习技术，用于学习时空网络数据的子结构感知表示。研究方法将受到深度表示学习和优化中的模型正则化的长期积极研究的推动。该项目将引入子结构意识的概念，这是一种动态正则化机制，用于强制表示学习模型关注子结构模式。该项目将解决两个基本的研究挑战：1)我们如何在表示学习中建模子结构知识？2)时间依赖性如何改善子结构感知表征学习？研究目标将由交通网络、社会网络和电网数据的综合评估计划来补充。这项研究将为动态图正则化和知识引导机器学习提供初步的探索和见解。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。

Spatiotemporal networked data are an essential representation of information about critical infrastructures such as transportation networks, power grids, and social networks. The evolving vehicle mobility of a transportation network can locate the source of traffic jams. The dynamic retweet keywords of a social network may inform a novel disease outbreak. This project will develop novel techniques to equip machines with automated and precision characterization with spatiotemporal networks. The main novelty of this project will be in its ability to preserve substructure patterns in characterization of spatiotemporal networked data. By recognizing and characterizing these substructure patterns such as a subnetwork of traffic jam, a subnetwork of overload or outage, computers can better extract semantics, forecast trends, and detect anomalies, which are important for operations, management, and defense of critical infrastructures. In transportation operations, the developed techniques have the potential to change how civil engineers identify the behavioral factors and surrounding features of precursor to crashes, fatalities, and accidents. For the researchers of power grid management, the automated and precision characterization approaches can help to inform the counter measures and characteristics of outrage events, such as generation loss, large load, series capacitor fault, and line trip. In public health and pandemics, the substructure awareness will enable the fast and early detection of novel diseases from subtle natural language patterns in social networks. This project will develop novel machine learning technologies for learning substructure-aware representations of spatiotemporal networked data. The research methodology will be motivated by long-stand and active research of deep representation learning and model regularization in optimization. The project will introduce the concept of substructure awareness which is a dynamic regularization mechanism to enforce representation learning models to pay attention to substructure patterns. This project will address two fundamental research challenges: 1) How can we model substructure knowledge in representation learning? 2) How can temporal dependencies improve substructure-aware representation learning? The research aims will be complemented by a comprehensive evaluation plan with transportation networks, social networks, and power grid data. This research effort will provide preliminary exploration and insights into dynamic graph regularization and knowledge-guided machine learning.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.