A Generative Pre-Training Framework for Spatio-Temporal Graph Transfer Learning
ICLR 2024(2024)
摘要
Spatio-temporal graph (STG) learning is foundational for smart city
applications, yet it is often hindered by data scarcity in many cities and
regions. To bridge this gap, we propose a novel generative pre-training
framework, GPDiff, for STG transfer learning. Unlike conventional approaches
that heavily rely on common feature extraction or intricate transfer learning
designs, our solution takes a novel approach by performing generative
pre-training on a collection of model parameters optimized with data from
source cities. We recast STG transfer learning as pre-training a generative
hypernetwork, which generates tailored model parameters guided by prompts,
allowing for adaptability to diverse data distributions and city-specific
characteristics. GPDiff employs a diffusion model with a transformer-based
denoising network, which is model-agnostic to integrate with powerful STG
models. By addressing challenges arising from data gaps and the complexity of
generalizing knowledge across cities, our framework consistently outperforms
state-of-the-art baselines on multiple real-world datasets for tasks such as
traffic speed prediction and crowd flow prediction. The implementation of our
approach is available: https://github.com/PLUTO-SCY/GPDiff.
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关键词
Spatio-temporal Graph,Transfer learning,Diffusion models,Pre-Training
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