IPN 2013 - 2013 Symposium on: Information Processing over Networks

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