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

该项目旨在开发新的统计和计算方法，以解决人类动力学中正在出现的问题，包括检测人类流动性的异常，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痕迹、社交媒体帖子等数字流动性数据的激增，结合统计学习的卓越预测能力，引发了以个体流动性为代理来研究人类动力学的深度学习的应用。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移植的方法，启发大数据分析领域的新研究，并为学生提供参与前沿和跨学科大数据研究的独特机会。该项目将在项目的三年中每年支持每所大学的一名研究生。该项目使用手机动态图形等代理来开发可用于威胁检测和传染病预测的人体动力学模型。移动性数据自然被表示为动态图，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。一个具体的应用是使用由数以百万计的手机用户的轨迹组成的安全图数据来预测新冠肺炎的超级传播者和感染人数，这对控制病毒的传播在临床上是必不可少的。本研究项目需要探讨的问题包括：(1)如何将节点(或子图)级时空信息演变的动态图编码为低维嵌入向量，作为后续预测和推理的特征输入；(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传递知识，以预测观测较少的新位置的疫情趋势。这个项目将解决这些问题，并开发一个大型动态图形中归纳空间编码的通用框架，使不同位置或任务的可解释和可转移学习成为可能。在动态图建模中开发的快速、可转移的计算原理是“大数据”计算和自治系统的基本和不可或缺的工具。这些原则将广泛适用于科学、工程和人文的不同领域。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.