2016
Unsupervised Learning from Noisy Networks with Applications to Hi-C Data
NeurIPS 2016poster
Complex networks play an important role in a plethora of disciplines in natural sciences. Cleaning up noisy observed networks, poses an important challenge in network analysis Existing methods utilize labeled data to alleviate the noise effect in the network. However, labeled data is usually expens…