Title: Machine learning on signed networks and time series analysis with applications to finance
Abstract: We discuss scalable spectral methods for detecting hidden structures in large signed/directed networks, with an eye towards robustness under sampling sparsity and noise perturbation. As an application, we consider the problem of propagating news sentiment in a financial network. When considering the universe of SP500 instruments (stocks), only about one third of the instruments have news sentiment released on a typical trading day. This raises the question of how does the disseminated news sentiment impact the remaining set of instruments. We proposes fast algorithms for understanding how news sentiment propagates through a financial correlation network. Our approaches are broadly applicable to instances where one has available a sparse signal (e.g., news sentiment, for a subset of nodes) and would like to understand how the available signal measurements propagate through the network to the remaining nodes. We formulate this problem as an instance of the group synchronization problem over Z2 with anchor information. Time permitting, we discuss potential extensions that leverage directed graph clustering algorithms from the lead-lag detection literature.ork on human-in-the-loop mining.