Inference on Networks: Algorithms, Phase Transitions, New Models and New Data

Abstract.  Networks provide a rigorous representation for both modeling and understanding the structure and function of complex systems. Over the past 10 years, researchers working in statistical inference and statistical physics have developed powerful new algorithms for understanding network data, and have revealed new phenomena in the behavior of these algorithms. This SFI workshop will bring together world experts in network science, statistical inference, and statistical physics to present recent advances in inference on networks and to organize future efforts on developing a deeper mathematical understanding of probability on networks, message-passing algorithms, and random matrices, on developing new models to handle new sources of rich data, and on applying these ideas to key questions in complex systems.