Unified Spatio-Temporal Graph Neural Networks: Data-Driven Modeling for Social Science

Yifan Li, Yu Lin, Yang Gao, Latifur Khan

Abstract: Time series forecasting with additional spatial de-pendencies has attracted a tremendous amount of research interest in social sciences, due to its importance in modern real-world applications. The Graph Neural Networks (GNN) is one of the most exciting deep learning techniques among these spatio-temporal modeling approaches. Most existing spatio-temporal GNN frameworks are based on a two-step modeling process. In such scenario, spatial and temporal dependencies are modeled in separate steps, which lead to problems such as complex architecture design, hard to scale, etc. Targeting the shortcomings of existing studies, we take both spatial and temporal dependencies from another perspective, and consider them as two heterogeneous types of edges in the graph. We propose a unified spatio-temporal GNN framework that captures both dependencies in a single step. More specifically, for each node in the graph, a unified neural network component is designed to simultaneously extract information from its sur-rounding neighbors (spatial) and its past records (temporal), which enables much easier dependency aggregation with faster execution. Experiment results demonstrate the superiority of the proposed framework over state-of-the-art (SOTA) baselines on various applications, including modeling smart cities and data-driven political science research.