Year: 2025
Type: conference-paper
Abstract: Spatial-temporal graphs are widely used in a variety of real-world applications. Spatial-Temporal Graph Neural Networks (STGNNs) have emerged as a powerful tool to extract meaningful insights from this data. However, in real-world applications, most nodes may not possess any available temporal data during training. For example, the pandemic dynamics of most cities on a geographical graph may not be available due to the asynchronous nature of outbreaks. Such a phenomenon disagrees with the training requirements of most existing spatial-temporal forecasting methods, which jeopardizes their effectiveness and thus blocks broader deployment. In this paper, we propose to formulate a novel problem of inductive forecasting with limited training data. In particular, given a spatial-temporal graph, we aim to learn a spatial-temporal forecasting model that can be easily generalized onto those nodes without any available temporal training data. To handle this problem, we propose a principled framework named ST-FiT. ST-FiT consists of two key learning components: temporal data augmentation and spatial graph topology learning. With such a design, ST-FiT can be used on top of any existing STGNNs to achieve superior performance on the nodes without training data. Extensive experiments verify the effectiveness of ST-FiT in multiple key perspectives.
Authors: Zhenyu Lei, Yushun Dong, Jundong Li, Chen Chen
Institutions: University of Virginia, Florida State University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Awards: SCC-IRG Track 1: Community-Responsive Electrified and Adaptive Transit Ecosystem (CREATE): Planning, Operations, and Management, Collaborative Research: III: Small: Graph-Oriented Usable Interpretation, CAREER: Toward A Knowledge-Guided Framework for Personalized Decision Making, III: Small: Advances in Federated Graph Machine Learning: A Data Perspective, Collaborative Research: SAI-R: Dynamical Coupling of Physical and Social Infrastructures: Evaluating the Impacts of Social Capital on Access to Safe Well Water, SaTC: CORE: Small: Empowering Network Attack Detection with Complex Graph Modeling, III: Small: Collaborative Research: Demystifying Deep Learning on Graphs: From Basic Operations to Applications
OpenAlex ID: W4409365561