Content Gazette - July 2013 - 39

CALL FOR PAPERS
Special Issue on Signal Processing for Social Networks
of the
IEEE Journal of Selected Topics in Signal Processing
Networks are fundamental to our modern world: they appear throughout science and society, and
continue to grow in size, complexity and importance. Whenever entities and relationships
between them are observed, a network is defined. As structural objects composed of nodes and
links, networks play a strong and well-defined role across mathematics, science and engineering.
However, significant advances in mathematical knowledge and understanding are required if a
holistic set of theory and methods is to be developed for signal processing on graphs and
networks-particularly social networks. In this realm, an important byproduct of the emergence
of on-line social media is the phenomenon that most things that are done on-line are recorded
instantly: be it web-clicks, transactions, wall-posts on Facebook, tweets, or blogs. In the past year
alone, 1.8 zettabytes (1021) of data have been generated in this way. The access to this massive
amount of social data presents a unique opportunity to the signal processing community.
As networks grow in size and complexity, the ability to analyze them using modern signal
processing methods is at substantial risk of failing to keep pace. This special issue, "Signal
Processing for Social Networks," will focus on a core set of signal processing fundamentals for
social networks, to provide both the necessary strong theoretical underpinnings and also the
practical tools required to impact important practical applications.
Potential topics include, but are not limited to:
*
*
*
*
*
*
*
*

Multi-agent estimation, detection and active decision-making in which agents
communicate over (a possibly random) graph
Handling, analysis, and visualization of "big data" from social networks
Statistical signal processing and machine learning for social network data
Detection and estimation theory with application to social networks
Models and inference algorithms for information spreading in social networks
Privacy and security & privacy preserving inference on user behavior from social
network data
Design, pricing and incentive mechanism for crowd-sourced system
Data-driven policy making

Guest Editors:
* Kwang-Cheng Chen, National Taiwan University (chenkc@ee.ntu.edu.tw)
* Vikram Krishnamurthy, University of British Columbia (vikramk@ece.ubc.ca)
* H. Vincent Poor, Princeton University (poor@princeton.edu)
* Devavrat Shah, Massachusetts Institute of Technology (devavrat@mit.edu)
* Patrick J. Wolfe, University College London (p.wolfe@ucl.ac.uk)
Schedule:
* Manuscript submission due: September 15, 2013
* First review due: December 1, 2013
* Revised manuscript due: January 15, 2014
* Second review due: March 1, 2014
* Final manuscript due: April 1, 2014
For Information on Paper Submission Please Visit the Following Site:
http://www.signalprocessingsociety.org/publications/periodicals/jstsp/jstsp-author-info/

www.signalprocessingsociety.org

[39]

JULY 2013


http://www.signalprocessingsociety.org/publications/periodicals/jstsp/jstsp-author-info/ http://www.signalprocessingsociety.org

Table of Contents for the Digital Edition of Content Gazette - July 2013

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Content Gazette - July 2013 - Cover3
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