GlobalSIP 2013 Symposium on:

Information Processing over Networks

[Download the PDF Call for Papers]

The symposium is focused on advances in network science and on contributions to the broad field of signal and information processing over graphs. Graphical models are prevalent in modern science and they help model various forms of interactions over complex networks, such as biological and social networks, and over engineered networks such as power grids and transportation and communications networks. In many instances, especially over networks encountered in nature, it is common for emergent behavior to emerge from the interactions among individual agents of limited capabilities as happens, for example, with fish schooling or bird flight formations. Research efforts to decipher the intricacies of complex networks have been progressing almost independently across several disciplines including system science, life sciences, social sciences, and computer science. There are ample opportunities for cross-disciplinary interactions and collaborations in order to understand and reverse-engineer the decentralized intelligence encountered in socio-economic-biological networks. This call for papers encourages submissions from a broad range of experts that study fundamental questions related to the problems of distributed inference, adaptation, learning, optimization, control, and information processing over graphs. Works that model and study self-organized and complex behavior encountered in nature and in the social and economic sciences are also welcome.

Submissions of at most 4 pages in two-column IEEE format are welcome on topics including:

  • Advances in network science
  • Bio-inspired distributed processing
  • Biological networks
  • Distributed adaptation
  • Distributed control mechanisms
  • Distributed detection and inference
  • Distributed estimation and filtering
  • Distributed game-theoretic strategies
  • Distributed information processing
  • Distributed learning
  • Distributed optimization
  • Graphical models
  • Signal processing over graphs
  • Social networks
  • Random graph representations
  • Sparse graph representations

Keynote Speakers

Stephen C. Pratt, Arizona State University, Distributed Information Processing by Insect Societies

Insect societies are the leading examples of collective cognition by social groups. Much like a single animal, a colony of ants can evaluate its surroundings, process information, and make decisions. Cognition emerges from a network of interacting ants, just as individual cognition emerges from interactions among neurons in the brain. The special appeal of these societies is that their parts-individual insects-are themselves complex cognitive entities, providing a unique opportunity to study the interplay between information processing at these two levels. In this talk I will show how individual behavioral rules and communication networks allow many poorly informed ants to make effective collective decisions. I will further show how colonies amplify the limited cognitive capacity of single ants and how they evade certain irrational consequences of individual choice. Finally, I will consider the limits of collective cognition by exploring when it can improve performance by integrating multiple agents, and when it can instead lead to harmful information cascades.

Stephen C. Pratt received a Ph.D. in neurobiology and behavior from Cornell University, Ithaca, NY, in 1997. Since then, he has worked at Harvard University, the Massachusetts Institute of Technology, Princeton University, and the University of Bath, U.K. He is currently an Associate Professor in the School of Life Sciences at Arizona State University,Tempe, AZ. His research focuses on the emergence of complex social behavior in leaderless and decentralized animal groups, particularly social insects. He employs both theoretical and empirical approaches, and is also active in the development of bio-inspired algorithms for swarm robotics applications.

Christophe Chamley, Boston University, Dynamic Social Learning in Economics

Christophe Chamley received an MA in Mathematics at the University of Strasbourg (France) and PhD in economics at Harvard University. He is professor of economics at Boston University and director of studies at the EHESS (Paris). He is a fellow of the Econometric Society. He held visiting positions at Harvard University, MIT, Stanford University, University of Bonn, University Carlos III in Madrid, and University of Louvain. He published in all top journals in economics on the theory of optimal taxation and the public debt, history of public finances, monetary policy, economic fluctuations, social learning. He is the author of Rational Herds: Economic Models of Social Learning at Cambridge University Press.

Paper Submission

Submit papers of at most 4 pages in two-column IEEE format through the GlobalSIP website at All papers (contributed and invited) will be presented as posters.

Important Dates

Paper Submission DeadlineJune 15, 2013
Review Results AnnounceJuly 30, 2013
Camera-Ready Papers DueSeptember 7, 2013

Organizing Committee

General Co-Chair
Jose M. F. Moura
Carnegie Mellon University
General Co-Chair
Ali H. Sayed
University of California, Los Angeles
General Co-Chair
Qing Zhao
University of California, Davis