The Maximum Balanced Biclique Problem is a well-known graph model with relevant applications in diverse domains. This paper introduces a novel algorithm, which combines an effective constraint-based tabu search procedure and two dedicated graph reduction techniques. We verify the effectiveness of the algorithm on 30 classical random benchmark graphs and 25 very large real-life sparse graphs from the popular Koblenz Network Collection (KONECT). The results show that the algorithm improves the best-known results (new lower bounds) for 10 classical benchmarks and obtains the optimal solutions for 14 KONECT instances.
@article{arxiv.1705.07339,
title = {Combining tabu search and graph reduction to solve the maximum balanced biclique problem},
author = {Yi Zhou and Jin-Kao Hao},
journal= {arXiv preprint arXiv:1705.07339},
year = {2017}
}