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One of the important missions of the field of Digital Humanities is to integrate quantitative research methods into subjects within the Humanities. Social Network Analysis, which originated in Mathematics, has been extended to the fields of Social Science, as well as Humanities such as Linguistics and Literature. However, History appears to adopt this research method at a slower pace. Therefore, the purpose of this research is to comprehend the capabilities and constraints of Social Network Analysis in historical research. This research employs Social Network Analysis on a historical dataset, 'Duacensia', a component of the broader historical database 'Impressa Catholica Cameracens,' which aims to investigate the history of Catholic printing in the ecclesiastical province of Cambrai. Following necessary data preprocessing, the research begins by reconstructing historical migration through a Network map. Subsequently, it constructs a network based on people's collaborations to present a comprehensive overview of the printing industry in Douai, highlighting the influence of English Catholics. The study then proceeds to identify significant individuals within the industry, with a specific focus on the involvement of female printers. Finally, it explores various approaches to Network construction and concludes by providing an opportunity for interested scholars to further examine the Networks established by this research. The results show that Social Network Analysis is indeed worthy of application in future historical research. Its visualisation ability can be powerful in showcasing historical migration or clustering historical connections. Additionally, its metrics, when combined with statistical analysis, can provide scientific evidence that bolsters historians' observations and statements. Nonetheless, applying Social Network Analysis requires sufficient data, and in the case of historical data, some may be missing due to age. Consequently, in comparison to modern databases, outcomes of Social Network Analysis from historical data could be more inferential and need to be interpreted with cautious consideration of potential gaps and limitations stemming from incomplete or aged records.