Mathematical Theory, Method and Application in Graph Signal Processing
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 30 November 2025 | Viewed by 58
Special Issue Editor
Special Issue Information
Dear Colleagues,
Under the impetus of modern information processing technology, signal-processing objects are undergoing a critical period of transformation from classic regular time–space domain signals to non-Euclidean topological structure data. These high-dimensional data with complex network features exhibit non-uniform sampling characteristics and irregular neighborhood correlations, which lead to the applicability bottleneck of traditional signal processing methods based on the assumption of Euclidean space. To address this challenge, graph signal processing expands the discrete signal processing theory to the graph signal space and constructs a systematic analysis framework based on spectral graph theory. The core of this framework lies in deepening and elevating the classical signal processing concepts to graph signals to achieve precise characterization of the time–frequency characteristics of complex topological structure signals.
This Special Issue will provide a perspective and means for centralizing and disseminating new information from the vantage point of graph signal processing. The breadth of graph signal processing and the diversity of its applicability require that each paper contain a clear and motivated introduction accessible to all our readers.
Areas of theories, methods, and applications include the following:
- Graph neural network;
- Graph filtering;
- Graph spectrum analysis;
- Graph Fourier transform;
- Graph fractional Fourier transform;
- Graph linear canonical transform;
- Graph wavelet transform;
- Graph-based forecasting;
- Graph-based classification;
- Graph-based imputation;
- Graph-based anomaly detection.
Prof. Dr. Zhi-Chao Zhang
Guest Editor
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Keywords
- graph signal processing
- graph convolution
- time–frequency analysis
- wavelet
- neural network
- machine learning
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