低通图信号处理及其应用用户指南
A User Guide to Low-Pass Graph Signal Processing and its Applications
摘要：
图过滤器的概念可用于定义图数据的生成模型。事实上，从网络动态的许多示例中获得的数据可以被视为图过滤器的输出。通过这种解释，经典的信号处理工具，如频率分析，已成功地应用于图形数据的类似解释，为数据科学产生新的见解。以下是关于特定类别的图形数据的用户指南，其中生成的图形滤波器是低通的，即，滤波器衰减较高图形频率中的内容，同时保留较低频率中的内容。我们的选择是出于低通模型在应用领域，如社交网络，金融市场和电力系统的流行。我们说明了如何利用低通图过滤器的属性来学习图拓扑或识别其社区结构;通过采样有效地表示图数据，恢复丢失的测量值，并对图数据进行去噪;低通属性也被用作检测异常的基线。
英文摘要：
The notion of graph filters can be used to define generative models for graph data. In fact, the data obtained from many examples of network dynamics may be viewed as the output of a graph filter. With this interpretation, classical signal processing tools such as frequency analysis have been successfully applied with analogous interpretation to graph data, generating new insights for data science. What follows is a user guide on a specific class of graph data, where the generating graph filters are low-pass, i.e., the filter attenuates contents in the higher graph frequencies while retaining contents in the lower frequencies. Our choice is motivated by the prevalence of low-pass models in application domains such as social networks, financial markets, and power systems. We illustrate how to leverage properties of low-pass graph filters to learn the graph topology or identify its community structure; efficiently represent graph data through sampling, recover missing measurements, and de-noise graph data; the low-pass property is also used as the baseline to detect anomalies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
