Abstract

Real-world social networks are dynamic in na-ture.  Companies continue to collaborate, align strategically, acquire, and merge over time, and receive positive/negative impact from other com-panies. Consequently, their performance changes with time. If one can understand what types of net-work changes affect a company’s value, he/she can predict the future value of the company, grasp in-dustry innovations, and make business more suc-cessful.  However, it often requires continuous records of relational changes, which are often dif-ficult to track for companies, and the models of mining longitudinal network are quite complicated.In this study, we developed algorithms and a sys-tem to infer large-scale evolutionary company net-works from public news during 1981–2009. Then,based on how networks change over time, as well as the financial information of the companies, we predicted company profit growth. This is the first study of longitudinal network-mining-based com-pany performance analysis in the literature