Unsupervised Learning from Noisy Networks with Applications to Hi-C Data

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 the noise levels. However, labeled data is usually expensive to collect while unlabeled data can be gathered cheaply. In this paper, we propose an optimization framework to mine useful structures from noisy networks in an unsupervised manner. The key feature of our optimization framework is its ability to utilize local structures as well as global patterns in the network. We extend our method to incorporate multiresolution networks in order to add further resistance in the presence of high-levels of noise. The framework is generalized to utilize partial labels in order to further enhance the performance. We empirically test the effectiveness of our method in denoising a network by demonstrating an improvement in community detection results on multi-resolution Hi-C data both with and without Capture-C-generated partial labels.