Social networks are important. The range of relationships within a social network allows individuals to tap into important resources. Both the structure and the position of individuals within a social network have consequences for outcomes. Relational knowledge about the past offers an additional perspective on understanding concepts such as who held power in certain periods, positions, etc. Most of our knowledge about the past is not based on relational information. Moreover, identifying possible relations is an arduous task. Thanks to increasingly digital accessiblity of source and also of related data, we have new approaches to conduct such research-specificly how we can efficiently use computational tools to help us understand social networks of the past, as found in historical documents. The aim of this project is to fill this gap in knowledge about networks of the past by using computational methods.