Unsupervised robust nonparametric learning of hidden community properties

We develop robust and scalable methods to uncover global properties of communities hidden in large noisy networks. Consider the fundamental situation where the nodes or users in the network are split into two classes according to their opinion or preferences on a specific topic. Examples include support of a particular candidate in elections [1], or a level of interest in a particular topic, or a degree of support of certain statement. We call these two classes the "active" and "inactive" users, respectively. Motivated by real-world settings, we assume that the network of interest is too large to be processed manually, especially for each possible topic of interest. Therefore, activity observations of users are determined and delivered to us by a third-party algorithm called the crawler. Naturally, the crawler has its classification and learning errors that are not known to us. Therefore, we treat a general nonparametric case of the crawler error probabilities. Our goal is to learn global properties of communities of active and inactive users despite such noise and errors, in an unsupervised way, while additionally being robust to a strong adversary.