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

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