Efficient Mining of Large Maximal Bicliques

  • Guimei Liu
  • Kelvin Sim
  • Jinyan Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4081)


Many real world applications rely on the discovery of maximal biclique subgraphs (complete bipartite subgraphs). However, existing algorithms for enumerating maximal bicliques are not very efficient in practice. In this paper, we propose an efficient algorithm to mine large maximal biclique subgraphs from undirected graphs. Our algorithm uses a divide-and-conquer approach. It effectively uses the size constraints on both vertex sets to prune unpromising bicliques and to reduce the search space iteratively during the mining process. The time complexity of the proposed algorithm is O(nd Open image in new window N), where n is the number of vertices, d is the maximal degree of the vertices and N is the number of maximal bicliques. Our performance study shows that the proposed algorithm outperforms previous work significantly.


Search Space Size Constraint Frequent Itemset Mining Adjacency List Closed Itemsets 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Guimei Liu
    • 1
  • Kelvin Sim
    • 1
  • Jinyan Li
    • 1
  1. 1.Institute for Infocomm ResearchSingapore

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