Bridging hydraulics and graph signal processing: A new perspective to estimate water distribution network pressures

Zhou, Xiao; Liu, Shuming; Xu, Weirong; Xin, Kunlun; Wu, Yipeng; Meng, Fanlin

doi:10.1016/j.watres.2022.118416

Cited by 9 publications

(1 citation statement)

References 36 publications

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“…This exchange of messages can be interpreted as the implementation of one or more graph filters, and thus, graph filtering has the potential to be a useful framework for analyzing and synthesizing federated learning methods. 6) Regarding applications, graph filters and respective extensions have potential in power and water networks [34], [329], Internet of Things [32], and finance [330]. Finally, we remark that graphs represent only pairwise relationships between datapoints but complex networks and data may often be better represented by higher-order network structures [331] such as multi-relational graphs [332], cell or simplicial complexes [333]- [336], and hypergraphs [337]- [339].…”

Section: A Look Aheadmentioning

confidence: 99%

Graph Filters for Signal Processing and Machine Learning on Graphs

Isufi¹,

Gama²,

Shuman³

et al. 2022

Preprint

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Filters are fundamental in extracting information from data. For time series and image data that reside on Euclidean domains, filters are the crux of many signal processing and machine learning techniques, including convolutional neural networks. Increasingly, modern data also reside on networks and other irregular domains whose structure is better captured by a graph. To process and learn from such data, graph filters account for the structure of the underlying data domain. In this article, we provide a comprehensive overview of graph filters, including the different filtering categories, design strategies for each type, and trade-offs between different types of graph filters. We discuss how to extend graph filters into filter banks and graph neural networks to enhance the representational power; that is, to model a broader variety of signal classes, data patterns, and relationships. We also showcase the fundamental role of graph filters in signal processing and machine learning applications. Our aim is that this article serves the dual purpose of providing a unifying framework for both beginner and experienced researchers, as well as a common understanding that promotes collaborations between signal processing, machine learning, and application domains.

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