On finding the maximum edge biclique in a bipartite graph: a subspace clustering approach

Bipartite graphs have been proven useful in modeling a wide range of relationship networks. Finding the maximum edge biclique within a bipartite graph is a well-known problem in graph theory and data mining, with numerous realworld applications across different domains. We propose a probabilistic algorithm for finding the maximum edge biclique using a Monte Carlo subspace clustering approach. Extensive experimentation with both artificial and real-world datasets shows that the algorithm is significantly better than the state-of-the-art technique. We prove that there are solid theoretical reasons for the algorithm’s efficacy that manifest in a polynomial complexity of time and space. Keywords—Maximum edge bipartite subgraph; Biclique; Subspace clustering; Data mining; Graph mining