We are living in the era of data where there is a fast-growing need for the analysis and processing of data generated by complex networks such as biological, social and communication networks, to name a few. The development of models and tools for analysing data and capturing their complex relationships represents one of the most prominent research fields. Graph signal processing emerged as a powerful tool to analyse signals defined over the vertices of a graph by encoding the pairwise relationships among data through the presence of edges. However, to grasp multiway relations among the constitutive elements of a network such as for example protein-to-protein interaction networks or brain networks, we need to go beyond graphs by resorting to more complex topological descriptors. A promising new research direction is the development of signal processing tools over higher order structures such as hypergraphs and simplicial complexes.
Signal processing on topological spaces is a novel and promising research direction merging together signal processing and topological tools to provide a powerful framework for the analysis of complex, multiway relationships among data. Higher order-based representations of data recently paved the way for new research directions in the area of machine learning and neural networks.
This special issue aims at presenting the latest research advances in signal processing over higher order networks by gathering papers providing new methods, models and applications. The main goal is to identify ongoing research directions and new perspectives in this infant and vibrant research field.
Topics of interest include (but are not limited to):
- Processing over higher order networks such as hypergraphs, simplicial complexes: filtering, sampling, transforms, spectral analysis
- Recent advances in Graph Signal Processing: multi-layer graphs, multigraphs
- Topological data analysis on higher order networks
- Nonlinear, statistical and robust signal processing over higher order networks
- Topology inference from data
- Signal processing over higher order networks for machine learning
- Higher order neural networks and deep learning
- Applications to neuroscience, bioengineering and bioinformatics
- Applications to finance, economics and social networks
- Applications to image, speech and video processing
- Applications to transport, power and communication networks
Manuscript due: 31 May 2022
Lead Guest Editor:
Stefania Sardellitti, University of Rome, Italy.
Gonzalo Mateos, University of Rochester, US.
Michael T. Schaub, RWTH Aachen University, Germany.
Santiago Segarra, Rice University, US.
Before submitting your manuscript, please ensure you have carefully read the Instructions for Authors for EURASIP Journal on Advances in Signal Processing. The complete manuscript should be submitted through the EURASIP Journal on Advances in Signal Processing submission system. To ensure that you submit to the correct special issue please select the appropriate section in the drop-down menu upon submission. In addition, indicate within your cover letter that you wish your manuscript to be considered as part of the special issue on 'Signal Processing over Higher Order Networks'. All submissions will undergo rigorous peer review and accepted articles will be published within the journal as a collection.
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