论文标题
了解光谱图神经网络
Understanding Spectral Graph Neural Network
论文作者
论文摘要
近年来，由于传统的卷积过滤器对非欧几里得结构化数据的限制，近年来，图神经网络已通过飞跃和边界开发。光谱图理论主要使用代数方法研究基本图特性来分析图的邻接矩阵或拉普拉斯矩阵的光谱，该矩阵奠定了图形卷积神经网络的基础。该报告不仅仅是注释和独立的，它来自我的博士学位。第一年报告文献综述部分，它说明了图形卷积神经网络模型是如何以光谱图理论为动机的，并讨论了与基本面相关的主要基于光谱的模型。还审查了光谱域中定义的图形卷积神经网络的实际应用。
Graph neural networks have developed by leaps and bounds in recent years due to the restriction of traditional convolutional filters on non-Euclidean structured data. Spectral graph theory mainly studies fundamental graph properties using algebraic methods to analyze the spectrum of the adjacency matrix or Laplacian matrix of a graph, which lays the foundation of graph convolutional neural networks. This report is more than notes and self-contained which comes from my Ph.D. first-year report literature review part, it illustrates how the graph convolutional neural network model is motivated by spectral graph theory, and discusses the major spectral-based models associated with their fundamentals. The practical applications of the graph convolutional neural networks defined in the spectral domain are also reviewed.
