Content Gazette - September 2019 - 30

Call for Papers
IEEE Signal Processing Society
IEEE SIGNAL PROCESSING MAGAZINE
Special Issue on Graph Signal Processing: Foundations and Emerging Directions
As modern data is getting more heterogeneous and intricate, identifying and leveraging its intrinsic structure and geometry
emerges as a crucial task. The postulation of parsimonious models for the available information and the existing
interdependencies is critical not only to understand the data at hand, but also to define effective compression, reconstruction,
and inference architectures to process the available information. Graph signal processing (GSP) has approached this problem
by modeling the structure of the data using a graph and, then, viewing the available information as a signal defined on top of it.
A plethora of graph signals exists, with examples ranging from neurological activity patterns defined on top of brain networks
to the spread of epidemics over social networks. Most early GSP efforts assumed that the underlying network was known, and
then analyzed how the graph's algebraic and spectral characteristics impact the properties of the graph signals of interest. More
recently, the focus has been on setups where the graph is implicit and must be learned from the data itself. While the theoretical
and practical success achieved by GSP in the last years has been noticeable, many issues remain open, with, e.g., robust,
nonlinear, or higher-dimensional GSP being at their infancy.
This Special Issue aims at introducing recent GSP advances, identifying some of its emerging directions, and presenting
relevant practical problems that can be successfully addressed using GSP tools. The overall goal is to provide an overview of
the current state of GSP, making it accessible to a broader audience who will ultimately contribute to shape the future of the field.
Topics of interest include (but are not limited to):
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Theoretical foundations for GSP: Advanced models for graphs, graph signals and graph filters
Nonlinear GSP
Beyond graph models: Hyper-based and tensor-based GSP
Statistical and robust GSP
Graph topology inference, including directed graphs and applications to causality
Machine learning for graph signals and geometric data
Applications of SP over directed graphs to causal inference
Deep learning architectures for graph signals and geometric data
Algorithmic advances, distributed computations and large-scale graphs
Bioengineering, neuroscience and bioinformatics using GSP-tools
Communication, power, and transportation networks using GSP-tools
Finance, economics, and social networks using GSP-tools
Speech, image and video processing using GSP-tools

Submission Process

The Special Issue seeks to offer broad coverage of the field including most recent developments in both theory and
applications. Submissions of comprehensive overviews are strongly encouraged, as well as papers dealing with new and
emerging applications provided that are accessible for a broad audience. White papers are required, and full articles are invited
based on the review of white papers. Submissions will be reviewed according to the IEEE Signal Processing Magazine
guidelines, and should not have been published or under review elsewhere
Manuscripts should be submitted online at http://mc.manuscriptcentral.com/sps-ieee using the Manuscript Central interface,
see http://www.signalprocessingsociety.org/publications/periodicals/spm/ for guidelines and information.
Important Dates

White papers (4 pages) due: October 7, 2019
Invitation notification: November 15, 2019
Manuscripts due: February 15, 2020
Review results and decision notification: May 1, 2020

Revised manuscripts due: June 7, 2020
Final acceptance notification: July 7, 2020
Final manuscripts due: August 1, 2020
Publication due: November 1, 2020

Guest Editors:

Antonio G. Marques, King Juan Carlos University, Spain, antonio.garcia.marques@urjc.es
José M.F Moura, Carnegie Mellon University, USA, moura@ece.cmu.edu
Dimitri Van De Ville, EPFL and University of Geneva, Switzerland, dimitri.vandeville@epfl.ch
Rebecca Willett, University of Chicago, USA, willett@uchicago.edu
Negar Kiyavash, Georgia Institute of Technology, USA, negar.kiyavash@ece.gatech.edu

www.signalprocessingsociety.org

[30]

SEPTEMBER 2019


http://mc.manuscriptcentral.com/sps-ieee http://www.signalprocessingsociety.org/publications/periodicals/spm/ http://www.signalprocessingsociety.org

Content Gazette - September 2019

Table of Contents for the Digital Edition of Content Gazette - September 2019

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Content Gazette - September 2019 - 1
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