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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Efficient Mining of Large Maximal Bicliques
verfasst von : Guimei Liu, Kelvin Sim, Jinyan Li
Erschienen in: Data Warehousing and Knowledge Discovery
Verlag: Springer Berlin Heidelberg
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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
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.