This study employs social networks analysis to investigate scientific collaboration within the field of immunology and infectiology at the University of Belgrade, Faculty of Medicine. A primary dataset for this analysis is obtained using publicly available data about employees and their publications registered in the Scopus database between 1982 and 2020. A co-authorship network is constructed and analyzed using both integer and fractional counts for co-authorship. Basic network metrics reveal the network’s classification as a small-world network with medium density and high clustering. Further analysis focuses on the organizational division of authors into four departments, unveiling significant collaboration between authors across departments. Community detection techniques are employed to reveal finer community structures within the network. Assortativity analysis, calculation of the rich-club coefficient, and exploration of power-law properties indicate a healthy research environment with minimal centralization of node influence. Centrality and brokerage analyses distinguish researchers with numerous co-authors from those with fewer co-authors, validating the utility of both weighting schemes, while ranking the distinguished researchers by each of these criteria, thus giving further insight into the collaboration dynamics of the environment.