论文标题
进一步了解图形信号
To further understand graph signals
论文作者
论文摘要
图形信号处理（GSP）是一个框架，用于分析和处理图形结构化数据。许多研究工作的重点是开发工具，例如图形傅立叶变换（GFT），过滤器和神经网络模型来处理图形信号。在许多情况下，这种方法已成功地照顾了``信号处理''。在本文中，我们想强调``图形信号''自己。尽管使用从GFT得出的带宽概念的图形信号有特征，但我们想在这里争论图信号可能包含网络的隐藏几何信息，而与（Graph）傅立叶理论无关。我们将提供一个框架来理解此类信息，并证明有关``图形信号''的新知识如何帮助``信号处理''。
Graph signal processing (GSP) is a framework to analyze and process graph-structured data. Many research works focus on developing tools such as Graph Fourier transforms (GFT), filters, and neural network models to handle graph signals. Such approaches have successfully taken care of ``signal processing'' in many circumstances. In this paper, we want to put emphasis on ``graph signals'' themselves. Although there are characterizations of graph signals using the notion of bandwidth derived from GFT, we want to argue here that graph signals may contain hidden geometric information of the network, independent of (graph) Fourier theories. We shall provide a framework to understand such information, and demonstrate how new knowledge on ``graph signals'' can help with ``signal processing''.
