The proposed research, by leveraging data, develops novel methods to study how the patterns of human influence and relationship may impact their productivity. We use graph theory, NLP, and convex optimization to extract information from communication logs of individuals in financial and social systems. In this project, we build upon theories from sociology, namely structural balance theory—which describes the dynamics that govern the sentiment of interpersonal relationships—and assess the impact on stock traders' profitability. We propose to capture the dynamics using a time-varying Markov model, and prove the convergence rate for the proposed model. Furthermore, we find the factors leading to an individual becoming influential in the underlying social system and efficiently estimate an individual's influence on others on the basis of their expertise, communication contents, and interaction patterns.