CFP: IEEE 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:

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Call for Papers: T-SIPN Special Issue on Inference and Learning Over Networks

IEEE Signal Processing Society
IEEE Transactions on Signal and Information Processing over Networks
Special Issue on Inference and Learning Over Networks

Networks are everywhere. They surround us at different levels and scales, whether we are dealing with communications networks, power grids, biological colonies, social networks, sensor networks, or distributed Big Data depositories. Therefore, it is not hard to appreciate the ongoing and steady progression of network science, a prolific research field spreading across many theoretical as well as applicative domains. Regardless of the particular context, the very essence of a network resides in the interaction among its individual constituents, and Nature itself offers beautiful paradigms thereof. Many biological networks and animal groups owe their sophistication to fairly structured patterns of cooperation, which are vital to their successful operation. While each individual agent is not capable of sophisticated behavior on its own, the combined interplay among simpler units and the distributed processing of dispersed pieces of information, enable the agents to solve complex tasks and enhance dramatically their performance. Self-organization, cooperation and adaptation emerge as the essential, combined attributes of a network tasked with distributed information processing, optimization, and inference. Such a network is conveniently described as an ensemble of spatially dispersed (possibly moving) agents, linked together through a (possibly time – varying) connection topology. The agents are allowed to interact locally and to perform in-network processing, in order to accomplish the assigned inferential task. Correspondingly, several problems such as, e.g., network intrusion, community detection, and disease outbreak inference, can be conveniently described by signals on graphs, where the graph typically accounts for the topology of the underlying space and we obtain multivariate observations associated with nodes/edges of the graph. The goal in these problems is to identify/infer/learn patterns of interest, including anomalies, outliers, and existence of latent communities. Unveiling the fundamental principles that govern distributed inference and learning over networks has been the common scope across a variety of disciplines, such as signal processing, machine learning, optimization, control, statistics, physics, economics, biology, computer, and social sciences. In the realm of signal processing, many new challenges have emerged, which stimulate research efforts toward delivering the theories and algorithms necessary to (a) designing networks with sophisticated inferential and learning abilities; (b) promoting truly distributed implementations, endowed with real-time adaptation abilities, needed to face the dynamical scenarios wherein real-world networks operate; and (c) discovering and disclosing significant relationships possibly hidden in the data collected from across networked systems and entities. This call for papers therefore encourages submissions from a broad range of experts that study such fundamental questions, including but not limited to:

  • Adaptation and learning over networks.
  • Consensus strategies; diffusion strategies.
  • Distributed detection, estimation and filtering over networks.
  • Distributed dictionary learning.
  • Distributed game-theoretic learning.
  • Distributed machine learning; online learning.
  • Distributed optimization; stochastic approximation.
  • Distributed proximal techniques, sub-gradient techniques.
  • Learning over graphs; network tomography.
  • Multi-agent coordination and processing over networks.
  • Signal processing for biological, economic, and social networks.
  • Signal processing over graphs.

Prospective authors should visit http://www.signalprocessingsociety.org/publications/periodicals/tsipn/ for information on paper submission. Manuscripts should be submitted via Manuscript Central at http://mc.manuscriptcentral.com/tsipn-ieee.

Important Dates:

  • Manuscript submission: February 1, 2016
  • First review completed: April 1, 2016
  • Revised manuscript due: May 15, 2016
  • Second review completed: July 15, 2016
  • Final manuscript due: September 15, 2016
  • Publication: December 1, 2016

Guest Editors:

 

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)