Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal
  Graph Learning
Junfeng Hu Xu Liu Zhencheng Fan Yuxuan Liang Roger Zimmermann
Spatio-temporal graph learning is a fundamental problem in the Web of Things
era, which enables a plethora of Web applications such as smart cities, human
mobility and climate analysis. Existing approaches tackle different learning
tasks independently, tailoring their models to unique task characteristics.
These methods, however, fall short of modeling intrinsic uncertainties in the
spatio-temporal data. Meanwhile, their specialized designs limit their
universality as general spatio-temporal learning solutions. In this paper, we
propose to model the learning tasks in a unified perspective, viewing them as
predictions based on conditional information with shared spatio-temporal
patterns. Based on this proposal, we introduce Unified Spatio-Temporal
Diffusion Models (USTD) to address the tasks uniformly within the
uncertainty-aware diffusion framework. USTD is holistically designed,
comprising a shared spatio-temporal encoder and attention-based denoising
networks that are task-specific. The shared encoder, optimized by a
pre-training strategy, effectively captures conditional spatio-temporal
patterns. The denoising networks, utilizing both cross- and self-attention,
integrate conditional dependencies and generate predictions. Opting for
forecasting and kriging as downstream tasks, we design Gated Attention (SGA)
and Temporal Gated Attention (TGA) for each task, with different emphases on
the spatial and temporal dimensions, respectively. By combining the advantages
of deterministic encoders and probabilistic diffusion models, USTD achieves
state-of-the-art performances compared to deterministic and probabilistic
baselines in both tasks, while also providing valuable uncertainty estimates.