Statistical Inference for Complex Networks
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Statistical Inference for Complex Networks Workshop, December 3-5, 2008, Santa Fe NM
Organizers: Aaron Clauset (SFI) and Cris Moore (UNM & SFI)
Description: The motivation for the meeting is based mainly on our conviction that answering many of the most interesting scientific questions about the structure and function of complex networks (e.g., social, biological, and technological) now depends on taking a more data-driven approach to making inferences, testing theories, and revealing fundamental principles of organization. As such, the tools of machine learning, computer science and statistical physics seem the best suited for pushing the field in this direction, for example, by incorporating information about node- and edge-attributes, dynamic structures, etc.
This SFI workshop is intended to bring together a medium-sized group of researchers, drawn from machine learning, physics and several domains where networks are being used, who are doing interesting and productive things with networks, particularly from a methodological perspective, to think and talk critically about what the big scientific questions are, and where the field should be going to answer them. Our goal is to have the best people who are already working in this area come together at SFI for the workshop.
*Please note that attendance to this event is by invitation only.