基于空间方法的图神经网络模型_用于时空图建模的图神经网络模型 Graph WaveNet | 课程上新...

课程概要

本课程来自集智学园图网络论文解读系列活动。

是对论文《Graph WaveNet for Deep Spatial-Temporal Graph Modeling》的解读。

时空图建模 (Spatial-temporal graph modeling)是分析系统中组成部分的空间维相关性和时间维趋势的重要手段。已有算法大多基于已知的固定的图结构信息来获取空间相关性，而邻接矩阵所包含的连接关系并不能反应真实的节点间交互。此外，现有基于 RNN 和 CNN 的时域建模方式不能真正的捕捉其中所存在的长程相关。本文提出了一个新的图神经网络模型 Graph WaveNet 用于时空图建模。其中包括两个组件，一个是自适应依赖矩阵(adaptive dependency matrix)，通过节点嵌入进行训练，用来精确建模节点的空间相关性。另一个是堆叠的 1D 带孔卷积(stacked dilated 1D convolution)，增加了模型在时域的感受野的大小。通过两个交通流预测数据集的测试，Graph WaveNet 均能达到 state-of-the-art 的效果。

课程资料

论文题目：Graph WaveNet for Deep Spatial-Temporal Graph Modeling论文地址：https://arxiv.org/abs/1906.00121

课程讲师

王硕

王硕：2014年，毕业于东北大学模式识别与智能系统专业，2016年加入彩云天气，负责雾霾预报算法及系统。

论文原文摘要

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.

课程大纲

介绍了论文的基础信息和论文背景
介绍了本篇论文解决的科学问题——时空序列预测
分模块介绍了Graph WaveNet算法框架，同时说明每个模块相关的以往研究
介绍了对比模型，以及实验结果
展开相关的讨论，探讨文章算法的局限性
展示相关资源列表

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