论文标题
在图神经网络中桥接光谱和空间域之间的差距
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks
论文作者
论文摘要
本文旨在通过弥合图形卷积的光谱和空间设计之间的差距来重新审视图形卷积神经网络。从理论上讲，我们在空间或光谱域中设计了图形卷积过程的一定等效性。所获得的一般框架允许对最受欢迎的Convgnn进行频谱分析，解释其性能并显示其极限。此外，所提出的框架用于在光谱域中设计新的卷积，并在空间域中应用自定义频率轮廓。我们还提出了图形卷积网络的深度可分离卷积框架的概括，这允许通过保持模型的容量来减少可训练参数的总数。据我们所知，这种框架从未在GNNS文献中使用。我们的建议对跨性和归纳图学习问题进行了评估。获得的结果显示了所提出的方法的相关性，并提供了第一个实验性证据之一，即光谱滤波器系数从一个图转移到另一个图。我们的源代码可公开可用：https：//github.com/balcilar/spectral-designed-graph-convolutions
This paper aims at revisiting Graph Convolutional Neural Networks by bridging the gap between spectral and spatial design of graph convolutions. We theoretically demonstrate some equivalence of the graph convolution process regardless it is designed in the spatial or the spectral domain. The obtained general framework allows to lead a spectral analysis of the most popular ConvGNNs, explaining their performance and showing their limits. Moreover, the proposed framework is used to design new convolutions in spectral domain with a custom frequency profile while applying them in the spatial domain. We also propose a generalization of the depthwise separable convolution framework for graph convolutional networks, what allows to decrease the total number of trainable parameters by keeping the capacity of the model. To the best of our knowledge, such a framework has never been used in the GNNs literature. Our proposals are evaluated on both transductive and inductive graph learning problems. Obtained results show the relevance of the proposed method and provide one of the first experimental evidence of transferability of spectral filter coefficients from one graph to another. Our source codes are publicly available at: https://github.com/balcilar/Spectral-Designed-Graph-Convolutions
