Bipartite graphs provide a powerful framework for modeling complex relationships between two distinct sets of entities, where edges exclusively connect vertices from one set to the other. Clustering in bipartite networks enables the identification of meaningful communities based on the interactions between these sets. This is particularly useful in identifying communities of host proteins targeted by virus proteins by analyzing their bipartite protein–protein interaction networks. This chapter focuses on community detection within bipartite virus–host protein interaction networks, employing Python’s NetworkX package. By using the Louvain and Leiden algorithms, we detect and analyze clusters of proteins, offering valuable insights into the structure and dynamics of these interactions. The methodology is presented with step-by-step instructions, making it applicable to a wide range of viral–host interaction data and in general any bipartite graph. This approach not only uncovers key protein communities but also contributes to the understanding of their potential biological significance, facilitating the identification of novel targets for therapeutic interventions.