2017/05/20 by Yi Zhou, Zhou, Yi, Jin‐Kao Hao +1
Biochemistry, Genetics and Molecular Biology · Computer Science · #Artificial Intelligence (cs.AI) #Computational Drug Discovery Methods #FOS: Computer and information sciences #Plant biochemistry and biosynthesis
paper · pdf · doi:10.48550/arxiv.1705.07339
openalex publication_date 2017/05/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.