Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and
taxi demand prediction, is an important task in deep learning area. However,
for the nodes in graph, their ST patterns can vary greatly in difficulties for
modeling, owning to the heterogeneous nature of ST data. We argue that
unveiling the nodes to the model in a meaningful order, from easy to complex,
can provide performance improvements over traditional training procedure. The
idea has its root in Curriculum Learning which suggests in the early stage of
training models can be sensitive to noise and difficult samples. In this paper,
we propose ST-Curriculum Dropout, a novel and easy-to-implement strategy for
spatial-temporal graph modeling. Specifically, we evaluate the learning
difficulty of each node in high-level feature space and drop those difficult
ones out to ensure the model only needs to handle fundamental ST relations at
the beginning, before gradually moving to hard ones. Our strategy can be
applied to any canonical deep learning architecture without extra trainable
parameters, and extensive experiments on a wide range of datasets are conducted
to illustrate that, by controlling the difficulty level of ST relations as the
training progresses, the model is able to capture better representation of the
data and thus yields better generalization.

提出了一种名为 ST-Curriculum Dropout 的新颖策略用于空间 - 时间图模型中，通过控制模型训练过程中 ST 关系的难度级别，从简单到复杂有序地将节点展示给模型，使其能够更好地捕捉数据表示，从而提高模型的泛化性能。

Easy Begun is Half Done: ST-Curriculum Dropout 下的时空图建模

Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout

Spatial-temporal graph modeling is an important task to analyze the spatial
relations and temporal trends of components in a system. Existing approaches
mostly capture the spatial dependency on a fixed graph structure, assuming that
the underlying relation between entities is pre-determined. However, the
explicit graph structure (relation) does not necessarily reflect the true
dependency and genuine relation may be missing due to the incomplete
connections in the data. Furthermore, existing methods are ineffective to
capture the temporal trends as the RNNs or CNNs employed in these methods
cannot capture long-range temporal sequences. To overcome these limitations, we
propose in this paper a novel graph neural network architecture, Graph WaveNet,
for spatial-temporal graph modeling. By developing a novel adaptive dependency
matrix and learn it through node embedding, our model can precisely capture the
hidden spatial dependency in the data. With a stacked dilated 1D convolution
component whose receptive field grows exponentially as the number of layers
increases, Graph WaveNet is able to handle very long sequences. These two
components are integrated seamlessly in a unified framework and the whole
framework is learned in an end-to-end manner. Experimental results on two
public traffic network datasets, METR-LA and PEMS-BAY, demonstrate the superior
performance of our algorithm.

本文介绍了一种新型的图神经网络，Graph WaveNet，它采用了自适应依赖矩阵和扩张卷积组件，能够精确捕捉数据中的隐藏空间依赖关系和处理非常长的时间序列，通过在两个公共交通网络数据集中的实验结果，证明了我们算法的优越性。