【论文阅读】Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting[时空图卷积网络: 用于交通预测的深度学习框架]（5）
原文地址：https://transport.ckcest.cn/Search/get/298151?db=cats_huiyi_jtxs
5. Related Works（相关工作）
There are several recent deep learning studies that are also motivated by the graph convolution in spatio-temporal tasks. Seo et al. [2016] introduced graph convolutional recurrent network (GCRN) to identify jointly spatial structures and dynamic variation from structured sequences of data. The key challenge of this study is to determine the optimal combinations of recurrent networks and graph convolution under specific settings. Based on principles above, Li et al. [2018] successfully employed the gated recurrent units (GRU) with graph convolution for long-term traffic forecasting. In contrast to these works, we build up our model completely from convolutional structures; The ST-Conv block is specially designed to uniformly process structured data with residual connection and bottleneck strategy inside; More efficient graph convolution kernels are employed in our model as well.
最近有几项深度学习研究也受到时空任务中的图卷积的启发。Seo et al. [2016] 引入了图卷积递归网络(GCRN)，从数据的结构化序列中联合识别空间结构和动态变化。本研究的关键挑战是确定在特定设置下递归网络和图卷积的最优组合。基于上述原理，Li et al. [2018] 成功地将门控循环单元(GRU)与图卷积结合用于长期交通预测。与这些作品相反，我们完全从卷积结构建立我们的模型;ST-Conv模块是专门设计的，用于统一处理内部带有剩余连接和瓶颈策略的结构化数据;在我们的模型中使用了更有效的图卷积核。
6. Conclusion and Future Work（结论与未来的工作）
In this paper, we propose a novel deep learning framework STGCN for traffic prediction, integrating graph convolution and gated temporal convolution through spatio-temporal convolutional blocks. Experiments show that our model outperforms other state-of-the-art methods on two real-world datasets, indicating its great potentials on exploring spatio-temporal structures from the input. It also achieves faster training, easier convergences, and fewer parameters with flexibility and scalability. These features are quite promising and practical for scholarly development and large-scale industry deployment. In the future, we will further optimize the network structure and parameter settings. Moreover, our proposed framework can be applied into more general spatio-temporal structured sequence forecasting scenarios, such as evolving of social networks, and preference prediction in recommendation systems, etc.
本文提出了一种新的用于交通预测的深度学习框架STGCN，通过时空卷积块集成图卷积和门控时间卷积。实验表明，我们的模型在两个真实数据集上的表现优于其他最先进的方法，表明它在从输入数据探索时空结构方面具有巨大的潜力。它还实现了更快的训练、更容易的收敛和更少的参数，具有灵活性和可伸缩性。这些特性对于学术开发和大规模行业部署来说是非常有前景和实用的。未来，我们将进一步优化网络结构和参数设置。此外，我们提出的框架可以应用于更一般的时空结构序列预测场景，如社会网络的进化，以及推荐系统中的偏好预测等。
参考文献
[Seo et al., 2016] Youngjoo Seo, Michae¨l Defferrard, Pierre Vandergheynst, and Xavier Bresson. Structured sequence modeling with graph convolutional recurrent networks. arXiv preprint arXiv:1612.07659, 2016.
[Li et al., 2018] Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In ICLR, 2018.