Graphs are a representation form that have shown to be very useful to describe the geometric structure of biomedical network-structured data. Weights, associated to edge in the graph, often represent the similarity between the two vertices it connects. The values on the graph can be considered as a set of samples (one sample with each vertex in the graph): the graph signal.
However, processing these graph signals is still challenging. Signal processing on graphs should extend classical signal processing algorithms to signals with an underlying complex and irregular structure. For the two basic approaches to signal processing on graphs (one rooted in the spectral graph theory that is built upon the graph Laplacian matrix and the other one, the discrete signal processing on graphs, that is rooted on the algebraic signal processing theory and built on the graph shift operator) many questions remain. Can algorithms from the classical digital signal processing field be useful? How dependencies arising from the irregular data domain can be taken into account? How can we construct a weighted graph that captures the geometric structure of the data domain? How to generalize fundamental operators to the graph setting for biomedical applications? What are the needs in terms of methodological development?
We still have many questions to efficiently extract information from these graphs.
In spite of these questions, graph signal processing can find applications in many biomedical fields: processing of epidemiological data, brain imaging, neuroscience…
Topics for this Special Issue include, but are not limited to:
– Methodological studies on recent advances in graph signal processing and applications to the biomedical field. Topics of interest include: filtering, sampling, transforms, graph topology inference, higher-order graphs, learning over graphs, dynamic signals and/or dynamic graphs, etc.
– Applications of graphs or /and of signal processing on graphs for the biomedical field
– Graph signal processing studies bringing new perspectives for the biomedical field
Guest Editor: Anne Humeau-Heurtier
Email: anne.heurtier@univ-angers.fr
Submission Information:
Submission Deadline: 31 May 2024
Keywords: Graph signal processing, network science, signal processing, graphical models, neuroimaging
Please choose “Special Theme” paper type when submitting, and please use either the Technology or the Science template.
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