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Graph Signal Processing: Vertex Multiplication
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DOI：10.1109/LSP.2021.3086391.png)
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摘要
En 中文
On the Euclidean domains of classical signal processing, linking of signal samples to underlying coordinate structures is straightforward. While graph adjacency matrices totally define the quantitative associations among the underlying graph vertices, a major problem in graph signal processing is the lack of explicit association of vertices with an underlying coordinate structure. To make this link, we propose an operation, called the vertex multiplication (VM), which is defined for graphs and can operate on graph signals. VM, which generalizes the coordinate multiplication (CM) operation in time series signals, can be interpreted as an operator that assigns a coordinate structure to a graph. By using the graph domain extension of differentiation and graph Fourier transform (GFT), VM is defined such that it shows Fourier duality that differentiation and CM operations are duals of each other under Fourier transformation (FT). Numerical examples and applications are also presented.
Keyword:
Measurement
Location awareness
Fourier transforms
Frequency-domain analysis
Time series analysis
Directed graphs
Signal processing
Graph signal processing (GSP)
graph Fourier transform (GFT)
duality
coordinate multiplication
vertex multiplication
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期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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