ATD: Inductive Spatiotemporal Graph Encoding for Interpretable and Transferable Deep Learning with Application in Human Dynamics

该计划旨在发展新的统计和计算方法，以解决人类动力学中的新问题，包括检测人类活动的异常，预测潜在的灾难性损害，以及监测我们社会中的疾病爆发。目前，电话记录、GPS追踪、社交媒体帖子等数字移动数据的激增，加上统计学习的出色预测能力，引发了深度学习的应用，以个人的移动为代理来研究人类动态。该项目将为发现任何超大时空数据集中的异常事件提供可解释和可转移的方法，激发大数据分析的新研究方向，并为学生参与前沿和跨学科的大数据研究提供独特的机会。该项目将在三年的时间里每年为每所大学的一位研究生提供资助。本项目使用手机动态图等代理工具开发可用于威胁检测和传染病预测的人体动态模型。移动性数据自然地表示为动态图，其中任何单个节点表示一个位置或一组人，其连接对应于节点之间的移动性度量。异常节点或连接可用于检测威胁或灾难。一个具体的应用是利用由数百万移动电话用户的轨迹组成的安全图数据预测COVID-19的超级传播者和感染人数，这在临床上对控制病毒传播至关重要。本研究项目要探讨的问题包括：(1)如何将节点（或子图）级时空信息演化的动态图编码为低维嵌入向量，作为进一步下游预测和推断的特征输入；(2)如何量化动态图中节点和连接对病毒传播的重要性；(3)如何从源位置的移动数据和多源数据中转移知识，以预测观测较少的新位置的流行趋势。该项目将解决这些问题，并为大型动态图中的归纳空间编码开发一个通用框架，使不同位置或任务的可解释和可转移学习成为可能。动态图建模中发展的快速、可转移的计算原理是“大数据”计算和自治系统的基础和不可或缺的工具。这些原则将广泛适用于科学、工程和人文学科的各个领域。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。

This project aims to develop novel statistical and computational methods to address the emerging issues in human dynamics, including detecting anomalies in human mobility, predicting potential disastrous damage, and monitoring disease outbreak in our society. At present, the proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the outstanding predictive power of statistical learning, triggered the applications of deep learning by using the mobility of individuals as a proxy to study human dynamics. This project will provide interpretable and transferable methods for discovering unusual events in any super-large spatiotemporal data set, inspire a new line of research in big data analytics, and offer a unique opportunity for students to participate in cutting-edge and interdisciplinary big data research. This project will support one graduate student per year at each university for each of the three years of the project. This project uses a proxy such as mobile phone dynamic graphs to develop human dynamics models that can be used for threat detection and infectious disease prediction. Mobility data is naturally represented as a dynamic graph, where any individual node represents a location or a group of people, and its connections correspond to measures of mobility between the nodes. The anomaly node or connection can be used for detecting threats or disasters. One concrete application is predicting the super-spreaders and the number of infections of COVID-19 using the SafeGraph data that consists of the trajectory of millions of mobile phone users, which is clinically essential to harness the virus spreading. Questions to be explored in this research project include: (1) How to encode the dynamic graphs evolving spatiotemporal information at node (or subgraph) level into low-dimensional embedding vectors that can be used as feature inputs for further downstream prediction and inference (2) How to quantify the importance of nodes and connections in the dynamic graph for virus transmission (3) How to transfer knowledge from mobility data and multi-source data of the source locations to predict the epidemic trends for new locations with fewer observations. This project will address these questions and develop a general framework for inductive spatial encoding in large dynamic graphs, which enables interpretable, and transferable learning for different locations or tasks. The fast, transferable computing principles developed in dynamic graph modeling are fundamental and indispensable tools for “big data” computation and autonomous systems. The principles will be widely applicable to diverse fields of sciences, engineering, and humanities.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.