Content Gazette - May 2019 - 25

IEEE Transactions on Signal and Information Processing over Networks
Special Issue on Network Topology Inference
Making sense of large-scale datasets from a network-centric perspective will constitute a crucial step to
obtain new insights in various areas in science and engineering. Machine learning (ML), in particular, can
significantly benefit from graph-based representations, as they are instrumental to unveil inner data
structures that can properly guide, for instance, unsupervised or semi-supervised learning algorithms.
Similarly, the goal of graph signal processing (GSP) is to develop information processing algorithms that
fruitfully leverage the data's relational structure. Most GSP efforts to date assume that the underlying
network is known, and then analyze how the graph's algebraic and spectral characteristics impact the
properties of the graph signals of interest. However, such an assumption is at times untenable in practice
and a fundamental question is how to use information available from graph signals to learn the underlying
network structure or a judicious network model. Inferring the underlying network structure is a key step for
facilitating statistical learning, efficient signal representation, visualization, prediction, (nonlinear)
dimensionality reduction, and (spectral) clustering.
This special issue aims at gathering the latest advances on network topology inference or graph learning
methods. The goal is to selectively cover a diverse gamut of graph learning methods and application
domains chosen on the basis of importance and relevance to signal processing (SP) expertise. It will also
introduce readers to challenges and opportunities for SP and ML research in emerging topic areas at the
crossroads of modeling, learning, and control of complex behavior arising with large-scale networked
systems that evolve over time. Application-related submissions are especially welcome.
Topics relevant to the special issue include, but are not limited to:
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Graphical model selection with structural or physical network process constraints
Learning graphs from the observation of smooth, stationary, or diffused signals
Learning graph representations and embeddings
Scalable, online, and decentralized algorithms for graph learning
Probabilistic inference of network structure with uncertainties
Theoretical studies of sample complexity, consistency, and robustness
Tomographic network topology inference
Learning directed graphs and causal inference
Identifying the topology of dynamic networks and multi-layer graphs
Graph learning for efficient signal representation and dimensionality reduction
Neural networks for latent graph inference, network growth prediction, community detection
Applications to geometric deep learning and natural language processing
Applications to image and video processing, restoration, and compression
Applications to infrastructure networks, the Internet, 5G, transportation, and energy grids
Applications to neuroscience, systems biology, genomics, and bioinformatics
Applications to social and information networks, the WWW, and financial data

Prospective authors should follow the instructions given on the IEEE TSIPN webpages and submit their
manuscript through the web submission system at https://mc.manuscriptcentral.com/tsipn-ieee
Schedule:
Manuscript due:
First Review Completed:
Revised manuscript due:
Second Review Completed:
Final manuscript due:
Publication date:

June 1, 2019
August 1, 2019
September 1, 2019
October 1, 2019
October 15, 2019
January 2020

Guest Editors:
Sergio Barbarossa - Sapienza University of Rome, Italy (sergio.barbarossa@uniroma1.it)
Xiaowen Dong - University of Oxford, UK (xdong@robots.ox.ac.uk)
Vincent Gripon - IMT-Atlantique, France (vincent.gripon@imt-atlantique.fr)
Gonzalo Mateos (Lead GE) - University of Rochester, USA (gmateosb@ece.rochester.edu)

www.signalprocessingsociety.org

[25]

MAY 2019


https://mc.manuscriptcentral.com/tsipn-ieee http://www.signalprocessingsociety.org

Content Gazette - May 2019

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