Collaborative Research: OAC Core: Distributed Graph Learning Cyberinfrastructure for Large-scale Spatiotemporal Prediction

图神经网络（gnn）将深度神经网络的成功从独立数据点扩展到关系数据点，例如从广泛分布在不同空间位置的环境传感器（如湿度、温度、PM2.5等）现场收集的观测数据。虽然大多数现有工作都集中在相对较小的、精心策划的数据上进行概念验证，但在离线设置下，现实世界的科学研究和应用需要更强大的GNN模型，这些模型可以有效地从大规模、实时、地理分布（geo-distributed）和不同（异构）的数据中学习。该项目旨在为训练大空间gnn绘制一个全新的网络基础设施解决方案，以填补这一空白。该项目的成功将为依赖大规模时空预测的广泛领域科学应用提供一个克服基本计算和通信瓶颈的网络基础设施。所提出的算法和系统将是培养对在地理分布规模上设计大型机器学习系统的更深入理解、教学和培训学生和同行、为研究生和本科生提供新课程、研究和实习机会的理想选择。该项目旨在开发一套全面的图构建和划分方法、分布式学习算法和网络基础设施设计，以支持大规模gnn用于地理空间科学研究和应用中的真实时空数据。该项目将解决重大的研究挑战，包括：(1)在地理启发图深度学习框架内制定时空预测；(2)在庞大的地理分散数据集上实现高精度、高效和经济的时空预测任务；(3)整合空间相关性、空间异质性、空间计算并行性和地理通信效率。本研究主要围绕以下几个关键研究主题展开：(1)构建基于时空数据的图的通用框架，确定空间关系，填补缺失节点属性。(2)开发集中式时空图学习基础设施，利用多个边缘微数据中心进行协同GNN模型学习。(3)建立分散式时空图学习基础设施，支持分散式地理多任务学习，解决空间异质性问题。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。

Graph Neural Networks (GNNs) have extended Deep Neural Networks’ success from independent data points to relational data points, such as observations collected on-site from environmental sensors (e.g., humidity, temperature, PM2.5, etc.) widely distributed in different spatial locations. While most existing works focus on proof-of-concept on relatively small, well-curated data, with offline settings, real-world scientific research, and applications need more capable GNN models, which can effectively learn from large-scale, real-time, geographically distributed (geo-distributed) and diversely different (heterogeneous) data. This project aims to chart a radically new cyberinfrastructure solution for training large-spatial GNNs to fill this gap. The success of this project will provide a cyberinfrastructure that overcomes the fundamental computational and communication bottlenecks for a broad range of domain science applications that rely on massive spatiotemporal prediction. The proposed algorithms and systems will be ideal for cultivating a deeper understanding of designing large machine-learning systems at a geo-distributed scale, teaching and training students and peers, and providing graduate and undergraduate students with new courses, research, and internship opportunities. This project aims to develop a comprehensive set of graph construction and partitioning methods, distributed learning algorithms, and cyberinfrastructure designs to support large-scale GNNs for real-world spatiotemporal data in geospatial scientific research and applications. The project will address significant research challenges, including (1) formulating spatiotemporal prediction within a geographically inspired graph deep learning framework, (2) enabling highly accurate, efficient, and cost-effective spatiotemporal prediction tasks across vast, geographically dispersed datasets, and (3) integrating spatial correlation, spatial heterogeneity, spatial computing parallelism, and geographic communication efficiency. The research is organized around several key research themes: (1) Creating a universal framework for constructing graphs from spatiotemporal data, determining spatial relationships, and filling in missing node attributes. (2) Developing a centralized spatiotemporal graph learning infrastructure that leverages multiple edge micro-datacenters for collaborative GNN model learning. (3) Establishing a decentralized spatiotemporal graph learning infrastructure that supports decentralized geographical multitask learning to address spatial heterogeneity.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.