Deep learning, particularly convolutional neural networks (CNNs), has yielded rapid, significant improvements in computer vision and related domains. But conventional deep learning architectures perform poorly when data have an underlying graph structure, as in social, biological, and many other domains. This article explores 1) how graph signal processing (GSP) can be used to extend CNN components to graphs to improve model performance and 2) how to design the graph CNN architecture based on the topology or structure of the data graph.

中文翻译：

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图信号处理和深度学习：卷积、池化和拓扑

深度学习，尤其是卷积神经网络 (CNN)，在计算机视觉和相关领域取得了快速、显着的改进。但是当数据具有底层图结构时，传统的深度学习架构表现不佳，例如在社会、生物和许多其他领域。本文探讨了 1) 如何使用图信号处理 (GSP) 将 CNN 组件扩展到图以提高模型性能以及 2) 如何根据数据图的拓扑或结构设计图 CNN 架构。