Connectionists: Call for papers: Signal Processing over Higher Order Networks

[Michael Schaub]

Call for papers: Signal Processing over Higher Order Networks

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


For more information visit the following page
https://asp-eurasipjournals.springeropen.com/signalnetworks

Submission deadline: 15 March 2022