Prompt-Based Spatio-Temporal Graph Transfer Learning

TL;DR

This paper introduces STGP, a prompt-based framework for spatio-temporal graph transfer learning that enhances adaptability across diverse urban computing tasks with limited data, outperforming existing methods.

Contribution

The paper proposes a unified, task-agnostic spatio-temporal graph model with learnable prompts for effective cross-task and cross-domain transfer learning.

Findings

STGP outperforms state-of-the-art baselines by up to 10.7% in three tasks.

Unified template enables capturing shared dependencies across tasks.

Two-stage prompting effectively captures domain knowledge and task-specific properties.

Abstract

Spatio-temporal graph neural networks have proven efficacy in capturing complex dependencies for urban computing tasks such as forecasting and kriging. Yet, their performance is constrained by the reliance on extensive data for training on a specific task, thereby limiting their adaptability to new urban domains with varied task demands. Although transfer learning has been proposed to remedy this problem by leveraging knowledge across domains, the cross-task generalization still remains under-explored in spatio-temporal graph transfer learning due to the lack of a unified framework. To bridge the gap, we propose Spatio-Temporal Graph Prompting (STGP), a prompt-based framework capable of adapting to multi-diverse tasks in a data-scarce domain. Specifically, we first unify different tasks into a single template and introduce a task-agnostic network architecture that aligns with this template. This approach enables capturing dependencies shared across tasks. Furthermore, we employ learnable prompts to achieve domain and task transfer in a two-stage prompting pipeline, facilitating the prompts to effectively capture domain knowledge and task-specific properties. Our extensive experiments demonstrate that STGP outperforms state-of-the-art baselines in three tasks-forecasting, kriging, and extrapolation-achieving an improvement of up to 10.7%.