Bipartite covers simplify complicated network viewing by breaking the graph into two full subgraphs. Network analysis determines network structure using combinatorial methods. The concept of Km,n biclique covers is large here. This study examines the new usage of Km,n biclique coverings on discrete network analysis structures. Bilique features separate massive, dense networks in our combinatorial technique. This reveals huge network substructures and interconnections. We can build Km,n biclique covers quickly and increase their performance by studying their mathematics. Various graphs are used to test algorithms in real network situations. These include social, information, and organic networks. The data imply that Km,n biclique covers are beneficial since they’re easy to compute, scale, and reveal hidden styles in complicated systems.