Spatiotemporal learning plays a crucial role in mobile computing techniques
to empower smart cites. While existing research has made great efforts to
achieve accurate predictions on the overall dataset, they still neglect the
significant performance heterogeneity across samples. In this work, we
designate the performance heterogeneity as the reason for unfair spatiotemporal
learning, which not only degrades the practical functions of models, but also
brings serious potential risks to real-world urban applications. To fix this
gap, we propose a model-independent Fairness-aware framework for SpatioTemporal
Graph learning (FairSTG), which inherits the idea of exploiting advantages of
well-learned samples to challenging ones with collaborative mix-up.
Specifically, FairSTG consists of a spatiotemporal feature extractor for model
initialization, a collaborative representation enhancement for knowledge
transfer between well-learned samples and challenging ones, and fairness
objectives for immediately suppressing sample-level performance heterogeneity.
Experiments on four spatiotemporal datasets demonstrate that our FairSTG
significantly improves the fairness quality while maintaining comparable
forecasting accuracy. Case studies show FairSTG can counter both spatial and
temporal performance heterogeneity by our sample-level retrieval and
compensation, and our work can potentially alleviate the risks on
spatiotemporal resource allocation for underrepresented urban regions.

该研究论文提出了一种面向公平性的模型无关的时空图学习框架（FairSTG），利用先进样本与挑战性样本之间的协同混合来解决不公平的时空学习中的性能异质性问题，从而提高公平性质量并保持可比较的预测准确性，并能够通过样本级的检索和补偿对空间和时间性能异质性进行抵消，从而在为欠代表的城市地区进行时空资源分配时减轻风险。

FairSTG: 通过协作的样本级优化来对抗性能异质性

FairSTG: Countering performance heterogeneity via collaborative  sample-level optimization

Learning the embeddings for urban regions from human mobility data can reveal
the functionality of regions, and then enables the correlated but distinct
tasks such as crime prediction. Human mobility data contains rich but abundant
information, which yields to the comprehensive region embeddings for cross
domain tasks. In this paper, we propose multi-graph fusion networks (MGFN) to
enable the cross domain prediction tasks. First, we integrate the graphs with
spatio-temporal similarity as mobility patterns through a mobility graph fusion
module. Then, in the mobility pattern joint learning module, we design the
multi-level cross-attention mechanism to learn the comprehensive embeddings
from multiple mobility patterns based on intra-pattern and inter-pattern
messages. Finally, we conduct extensive experiments on real-world urban
datasets. Experimental results demonstrate that the proposed MGFN outperforms
the state-of-the-art methods by up to 12.35% improvement.

本文提出了一种名为多图融合网络（MGFN）的模型，通过人类移动数据的地理位置相似性融合模块，将人类移动数据的多种模式嵌入到该模型中，这有助于进行交叉领域的预测任务，如犯罪预测，并在实际城市数据集上进行了广泛的实验，结果表明，MGFN 模型优于现有技术方法，最大提高了 12.35％。