Efficient Exact Algorithms for Maximum Balanced Biclique Search in Bipartite Graphs

TL;DR

This paper introduces two specialized exact algorithms for finding maximum balanced bicliques in bipartite graphs, achieving near polynomial time in dense cases and efficient solutions in large sparse graphs, with extensive experimental validation.

Contribution

The paper presents novel algorithms tailored for dense and sparse bipartite graphs, significantly improving the speed of exact maximum balanced biclique search in real-world applications.

Findings

Dense bipartite graphs can be solved in near polynomial time.

Sparse graphs are handled by decomposing into dense subgraphs and applying specialized algorithms.

Algorithms efficiently process graphs with millions of vertices within seconds.

Abstract

Given a bipartite graph, the maximum balanced biclique (\textsf{MBB}) problem, discovering a mutually connected while equal-sized disjoint sets with the maximum cardinality, plays a significant role for mining the bipartite graph and has numerous applications. Despite the NP-hardness of the \textsf{MBB} problem, in this paper, we show that an exact \textsf{MBB} can be discovered extremely fast in bipartite graphs for real applications. We propose two exact algorithms dedicated for dense and sparse bipartite graphs respectively. For dense bipartite graphs, an $\mathcal{O}^{*}( 1.3803^{n})$ algorithm is proposed. This algorithm in fact can find an \textsf{MBB} in near polynomial time for dense bipartite graphs that are common for applications such as VLSI design. This is because, using our proposed novel techniques, the search can fast converge to sufficiently dense bipartite graphs which we prove to be polynomially solvable. For large sparse bipartite graphs typical for applications such as biological data analysis, an $\mathcal{O}^{*}( 1.3803^{\ddot{\delta}})$ algorithm is proposed, where $\ddot{\delta}$ is only a few hundreds for large sparse bipartite graphs with millions of vertices. The indispensible optimizations that lead to this time complexity are: we transform a large sparse bipartite graph into a limited number of dense subgraphs with size up to $\ddot{\delta}$ and then apply our proposed algorithm for dense bipartite graphs on each of the subgraphs. To further speed up this algorithm, tighter upper bounds, faster heuristics and effective reductions are proposed, allowing an \textsf{MBB} to be discovered within a few seconds for bipartite graphs with millions of vertices. Extensive experiments are conducted on synthetic and real large bipartite graphs to demonstrate the efficiency and effectiveness of our proposed algorithms and techniques.