Complex spatial dependencies in transportation networks make traffic
prediction extremely challenging. Much existing work is devoted to learning
dynamic graph structures among sensors, and the strategy of mining spatial
dependencies from traffic data, known as data-driven, tends to be an intuitive
and effective approach. However, Time-Shift of traffic patterns and noise
induced by random factors hinder data-driven spatial dependence modeling. In
this paper, we propose a novel dynamic frequency domain graph convolution
network (DFDGCN) to capture spatial dependencies. Specifically, we mitigate the
effects of time-shift by Fourier transform, and introduce the identity
embedding of sensors and time embedding when capturing data for graph learning
since traffic data with noise is not entirely reliable. The graph is combined
with static predefined and self-adaptive graphs during graph convolution to
predict future traffic data through classical causal convolutions. Extensive
experiments on four real-world datasets demonstrate that our model is effective
and outperforms the baselines.

本文提出了一种新颖的动态频域图卷积网络 (DFDGCN)，用于捕捉交通数据中的空间依赖性，通过傅里叶变换来缓解时间偏移的影响，结合静态预定义和自适应图，通过经典因果卷积预测未来的交通数据。经过四个真实世界数据集的广泛实验证明，我们的模型具有有效性，并且优于基准模型。

动态频域图卷积网络用于交通预测

Dynamic Frequency Domain Graph Convolutional Network for Traffic  Forecasting

Graph Gaussian Processes (GGPs) provide a data-efficient solution on graph
structured domains. Existing approaches have focused on static structures,
whereas many real graph data represent a dynamic structure, limiting the
applications of GGPs. To overcome this we propose evolving-Graph Gaussian
Processes (e-GGPs). The proposed method is capable of learning the transition
function of graph vertices over time with a neighbourhood kernel to model the
connectivity and interaction changes between vertices. We assess the
performance of our method on time-series regression problems where graphs
evolve over time. We demonstrate the benefits of e-GGPs over static graph
Gaussian Process approaches.

提出了一种新方法 ——evolving-Graph Gaussian Processes（e-GGPs），能够学习表示时间上的动态图顶点状态转移函数，并利用邻域内核模型来建模顶点之间的连通性和相互作用变化，成功地解决了 GGPs 在处理动态结构图像时遇到的局限性。