Be the first to like this
Networks are a natural representation of complex systems across domains, from social, to biological, to technological. Modeling the dynamics and structure of networks is central to the understanding of these systems and have a huge economic impact. However, the characteristics of network data captured from online complex systems present a number of challenges to the design and evaluation of machine learning methods. This talk will outline some of the important algorithmic and evaluation challenges that arise due to the massive size, streaming nature and heterogenous structure of complex networks, and discuss statistical online methods, unbiased and shrinkage estimators, as well as learning techniques to address some of these challenges.