Efficient Mining of Maximal Bicliques in Graph by Pruning Search Space

In this paper, we present a new algorithm for mining or enumerating maximal biclique (MB) subgraphs in an undirected general graph. Our algorithm achieves improved theoretical efficiency in time over the best algorithms. For an undirected graph with n vertices, m edges and k maximal bicliques, our algorithm requires O(kn2) time, which is the state of the art performance. Our main idea is based on a strategy of pruning search space extensively. This strategy is made possible by the approach of storing maximal bicliques immediately after detection and allowing them to be looked up during runtime to make pruning decisions. The space complexity of our algorithm is O(kn) because of the space used for storing the MBs. However, a lot of space is saved by using a compact way of storing MBs, which is an advantage of our method. Experiments show that our algorithm outperforms other state of the art methods.

@article{Kim2020,
  title     = {Efficient Mining of Maximal Bicliques in Graph by Pruning Search Space},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {3},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Youngtae Kim and Dongyul Ra},
  doi       = {10.14569/IJACSA.2020.0110356},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110356}
}