CFP: IEEE JSTSP and T-SIPN Special Issues on Graph Signal Processing

IEEE Journal of Selected Topics in Signal Processing
IEEE Transactions on Signal and Information Processing over Networks
Special Issues on Graph Signal Processing

Numerous applications rely on the processing of high-dimensional data that resides on irregular or otherwise unordered structures which are naturally modeled as networks (such as social, economic, energy, transportation, telecommunication, sensor, and neural, to name a few). The need for new tools to process such data has led to the emergence of the field of graph signal processing, which merges algebraic and spectral graph theoretic concepts with computational harmonic analysis to process signals on structures such as graphs. This important new paradigm in signal processing research, coupled with its numerous applications in very different domains, has fueled the rapid development of an inter-disciplinary research community that has been working on theoretical aspects of graph signal processing and applications to diverse problems such as big data analysis, coding and compression of 3D point clouds, biological data processing, and brain network analysis.

The purpose of these special issues is to gather the latest advances in graph signal processing and disseminate new ideas and experiences in this emerging field to a broad audience. We encourage the submission of papers with new results, methods or applications in graph signal processing. In particular, the topics of interest include (but are not limited to):

  • Sampling and recovery of graph signals
  • Graph filter and filter bank design
  • Uncertainty principles and other fundamental limits
  • Graph signal transforms
  • Graph topology inference
  • Prediction and learning in graphs
  • Statistical graph signal processing
  • Non-linear graph signal processing
  • Applications to visual information processing
  • Applications to neuroscience and other medical fields
  • Applications to economics and social networks
  • Applications to various infrastructure networks

Submission Procedure:
Prospective authors should follow the instructions given on the IEEE JSTSP webpages and submit their manuscript with the web submission system at https://mc.manuscriptcentral.com/jstsp-ieee. The decisions on whether the accepted papers will be published in IEEE JSTSP or IEEE TSIPN will depend on the respective themes of the papers and will be made by the Guest Editors.

Schedule (all deadlines are firm):

Manuscript due: Nov 1, 2016
First Review Completed: Jan 1, 2017
Revised manuscript due: Mar 1, 2017
Second Review Completed: May 1, 2017
Final manuscript due: June 1, 2017
Publication date: September 2017

Guest Editors:

  • Pier-Luigi Dragotti, Imperial College, London (p.dragotti@imperial.ac.uk)
  • Pascal Frossard, EPFL, Lausanne (pascal.frossard@epfl.ch)
  • Antonio Ortega, USC, Los Angeles (ortega@sipi.usc.edu)
  • Michael Rabbat, McGill University, Montreal (michael.rabbat@mcgill.ca)
  • Alejandro Ribeiro, UPenn, Philadelphia (aribeiro@seas.upenn.edu)
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Call for Papers: T-SIPN Special Issue on Distributed Information Processing in Social Networks

IEEE Signal Processing Society
IEEE Transactions on Signal and Information Processing over Networks
Special Issue on Distributed Information Processing in Social Networks

Over the past few decades, online social networks such as Facebook and Twitter have significantly changed the way people communicate and share information with each other. The opinion and behavior of each individual are heavily influenced through interacting with others. These local interactions lead to many interesting collective phenomena such as herding, consensus, and rumor spreading. At the same time, there is always the danger of mob mentality of following crowds, celebrities, or gurus who might provide misleading or even malicious information. Many efforts have been devoted to investigating the collective behavior in the context of various network topologies and the robustness of social networks in the presence of malicious threats. On the other hand, activities in social networks (clicks, searches, transactions, posts, and tweets) generate a massive amount of decentralized data, which is not only big in size but also complex in terms of its structure. Processing these data requires significant advances in accurate mathematical modeling and computationally efficient algorithm design. Many modern technological systems such as wireless sensor and robot networks are virtually the same as social networks in the sense that the nodes in both networks carry disparate information and communicate with constraints. Thus, investigating social networks will bring insightful principles on the system and algorithmic designs of many engineering networks. An example of such is the implementation of consensus algorithms for coordination and control in robot networks. Additionally, more and more research projects nowadays are data-driven. Social networks are natural sources of massive and diverse big data, which present unique opportunities and challenges to further develop theoretical data processing toolsets and investigate novel applications. This special issue aims to focus on addressing distributed information (signal, data, etc.) processing problems in social networks and also invites submissions from all other related disciplines to present comprehensive and diverse perspectives. Topics of interest include, but are not limited to:

  • Dynamic social networks: time varying network topology, edge weights, etc.
  • Social learning, distributed decision-making, estimation, and filtering
  • Consensus and coordination in multi-agent networks
  • Modeling and inference for information diffusion and rumor spreading
  • Multi-layered social networks where social interactions take place at different scales or modalities
  • Resource allocation, optimization, and control in multi-agent networks
  • Modeling and strategic considerations for malicious behavior in networks
  • Social media computing and networking
  • Data mining, machine learning, and statistical inference frameworks and algorithms for handling big data from social networks
  • Data-driven applications: attribution models for marketing and advertising, trend prediction, recommendation systems, crowdsourcing, etc.
  • Other topics associated with social networks: graphical modeling, trust, privacy, engineering applications, etc.

Important Dates:

Manuscript submission due: September 15, 2016
First review completed: November 1, 2016
Revised manuscript due: December 15, 2016
Second review completed: February 1, 2017
Final manuscript due: March 15, 2017
Publication: June 1, 2017

Guest Editors:

Zhenliang Zhang, Qualcomm Corporate R&D (zhenlian@qti.qualcomm.com)
Wee Peng Tay, Nanyang Technological University (wptay@ntu.edu.sg)
Moez Draief, Imperial College London (m.draief@imperial.ac.uk)
Xiaodong Wang, Columbia University (xw2008@columbia.edu)
Edwin K. P. Chong, Colorado State University (edwin.chong@colostate.edu)
Alfred O. Hero III, University of Michigan (hero@eecs.umich.edu)

Transactions on Signal and Information Processing Over Networks: now accepting papers

I’m an Associate Editor for the new IEEE Transactions on Signal and Information Processing Over Networks, and we are accepting submissions now. The Editor-In-Chief is Petar M. Djurić.

The new IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data.

To submit a paper, go to Manuscript Central.

Topics of interest include, but are not limited to the following:

Adaptation, Detection, Estimation, and Learning

  • Distributed detection and estimation
  • Distributed adaptation over networks
  • Distributed learning over networks
  • Distributed target tracking
  • Bayesian learning; Bayesian signal processing
  • Sequential learning over networks
  • Decision making over networks
  • Distributed dictionary learning
  • Distributed game theoretic strategies
  • Distributed information processing
  • Graphical and kernel methods
  • Consensus over network systems
  • Optimization over network systems

Communications, Networking, and Sensing

  • Distributed monitoring and sensing
  • Signal processing for distributed communications and networking
  • Signal processing for cooperative networking
  • Signal processing for network security
  • Optimal network signal processing and resource allocation

Modeling and Analysis

  • Performance and bounds of methods
  • Robustness and vulnerability
  • Network modeling and identification
  • Simulations of networked information processing systems
  • Social learning
  • Bio-inspired network signal processing
  • Epidemics and diffusion in populations

Imaging and Media Applications

  • Image and video processing over networks
  • Media cloud computing and communication
  • Multimedia streaming and transport
  • Social media computing and networking
  • Signal processing for cyber-physicalsystems
  • Wireless/mobile multimedia

Data Analysis

  • Processing, analysis, and visualization of big data
  • Signal and information processing for crowd computing
  • Signal and information processing for the Internet of Things
  • Emergence of behavior

Emerging topics and applications

  • Emerging topics
  • Applications in life sciences, ecology, energy, social networks, economic networks, finance, social sciences, smart grids, wireless health, robotics, transportation, and other areas of science and engineering