The maximal biclique enumeration (MBE) is a problem of identifying all maximal bicliques in a bipartite graph. Once enumerated in a bipartite graph, maximal bicliques can be used to solve problems in areas such as purchase prediction, statistic analysis of social networks, discovery of interesting structures in protein-protein interaction networks, identification of common gene-set associations, and integration of diverse functional genomes data. In this paper, we develop an optimized sequential MBE algorithm called sMBEA for sparse bipartite graphs which appear frequently in real life. The results of extensive experiments on several real-life data sets demonstrate that sMBEA outperforms the state-of-the-art sequential algorithm iMBEA.
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