Statistical methods for modeling complex networks have received much attention in the machine learning community in recent years. Models and inference tools have matured considerably and are increasingly applied in network science. Contributions include; expressive generative models, a wide range of methods for efficient approximate inference including MCMC and variational approaches, and principled ways to predict unobserved data, model uncertainty and validate structure. Applications have been pursued ranging from web scale social science to brain networks
This workshop aims at introducing machine learning techniques to a wider network science audience. The workshop will feature leading experts in machine learning in network data who will present their views and present the most important challenges modeling complex networks including: What is best practice for validating network models? What are the principal modeling challenges? Which models and techniques admit analysis of large scale networks?
If you plan to participate at the workshop, please pre-register at http://www.eventbrite.com/event/6288389743. Registration is free of charge but we kindly ask participants to register for organizational reasons.
The workshop will be located at the campus of the Technical University of Denmark, building 101, seminar room S12.