Bipartite graphs are useful for modeling relationship networks. A new algorithm using subspace clustering can find the largest connected group of nodes in these graphs more effectively than previous methods. This algorithm has been tested on both artificial and real datasets, showing its superiority. The algorithm's efficiency is supported by solid theoretical reasons, making it faster and requiring less memory to run.