EAGER: Collaborative Research: Substructure-aware Spatiotemporal Representation Learning

时空网络化数据是交通网络、电网、社交网络等重要基础设施信息的重要表示形式。交通网络中不断变化的车辆机动性可以定位交通堵塞的来源。社交网络的动态转推关键词可能会通知新的疾病爆发。该项目将开发新技术，为机器配备自动化和精确的时空网络表征。该项目的主要新奇将是它能够保持子结构模式的时空网络数据的表征。通过识别和表征这些子结构模式，如交通堵塞的子网络，过载或断电的子网络，计算机可以更好地提取语义，预测趋势，并检测异常，这对于关键基础设施的运营，管理和防御非常重要。在运输业务中，开发的技术有可能改变土木工程师识别行为因素和周围特征的方式，这些因素和特征是撞车、死亡和事故的前兆。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他们了解发电损失、大负荷、串联电容器故障和线路跳闸等异常事件的应对措施和特征。在公共卫生和流行病方面，子结构意识将能够从社交网络中微妙的自然语言模式中快速和早期地发现新疾病。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模型正则化的长期和积极研究的推动。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。这个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示学习？ 研究目标将通过一个综合评估计划与交通网络，社交网络，和电网数据的补充。这项研究工作将为动态图正则化和知识引导的机器学习提供初步探索和见解。该奖项反映了NSF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.