Can algorithms detect lost scholarly communities? A network-based study of the Lower Yangtze in Late Imperial China (1644–1912)

Abstract This article proposes a network-based framework for analyzing scholarly communities in Qing China (1644–1912), introducing the concept of “lost scholarly communities” to identify structurally cohesive but historiographically overlooked intellectual groups. Drawing on Qingru Xue’an (清儒學案, Cases of Qing Confucian Learning) as the primary source, supplemented by China Biographical Database and China Historical Geographic Information System, the study reconstructs over 3,000 academic relationships among approximately 1,300 scholars in the Jiangnan region. These relationships—spanning mentorship, social interaction, and correspondence—are extracted, structurally coded, and transformed into a dynamic temporal network. The methodological framework combines dynamic clustering to track the longitudinal aggregation of communities with static Louvain-based detection to reveal latent groupings across space and time. The study demonstrates that Qing intellectual communities did not always align with named schools or regional factions; instead, relational density and interactional structure better account for the emergence of scholarly cohesion. By identifying “lost communities” within the network, the research challenges conventional ideological or genealogical classifications and offers a relational redefinition of academic affiliation. More broadly, it provides a transferable model for historical knowledge network analysis, applicable to other periods and corpora. This approach complements textual and conceptual methods in intellectual history while expanding the digital humanities tool kit for mapping knowledge circulation in historical contexts.