论文标题
从频谱小波到顶点传播：基于泰勒近似的图形卷积网络
From Spectrum Wavelet to Vertex Propagation: Graph Convolutional Networks Based on Taylor Approximation
论文作者
论文摘要
Graph卷积网络（GCN）最近已被用来提取具有一些标记的数据和高维特征的数据集的基础结构。现有的GCN主要依赖于图形小波 - 内核的一阶Chebyshev近似。这样的通用传播模型并不总是适合各种数据集及其功能。这项工作重新审视了图小波的基本原理，并探讨了信号传播在顶点域中的实用性，以近似光谱小波 - 内核。我们首先通过顶点传播得出表示图形小波内核的条件。接下来，我们提出了基于泰勒膨胀的GCN层的替代传播模型。我们进一步分析了TGCN的详细图表的选择。在引文网络，多媒体数据集和合成图上进行的实验证明了基于泰勒的GCN（TGCN）在节点分类问题中比传统GCN方法的优势。
Graph convolutional networks (GCN) have been recently utilized to extract the underlying structures of datasets with some labeled data and high-dimensional features. Existing GCNs mostly rely on a first-order Chebyshev approximation of graph wavelet-kernels. Such a generic propagation model does not always suit the various datasets and their features. This work revisits the fundamentals of graph wavelet and explores the utility of signal propagation in the vertex domain to approximate the spectral wavelet-kernels. We first derive the conditions for representing the graph wavelet-kernels via vertex propagation. We next propose alternative propagation models for GCN layers based on Taylor expansions. We further analyze the choices of detailed graph representations for TGCNs. Experiments on citation networks, multimedia datasets and synthetic graphs demonstrate the advantage of Taylor-based GCN (TGCN) in the node classification problems over the traditional GCN methods.
