Finding the Maximum $k$-Balanced Biclique on Weighted Bipartite Graphs

Publication Name

IEEE Transactions on Knowledge and Data Engineering


Bipartite graphs are widely used to capture the relationships between two types of entities. In bipartite graph analysis, finding the maximum balanced biclique (MBB) is an important problem with numerous applications. A biclique is balanced if its two disjoint vertex sets are of equal size. However, in real-world scenarios, each vertex is associated with a weight to denote its properties, such as influence, i.e., weighted bipartite graph. For weighted bipartite graphs, the previous studies for MBB are no longer applicable due to the ignorance of weight. To fill the gap, in this paper, we propose a reasonable definition of “balance” by restricting the weight difference between two sides of a biclique within $k$. Given a weighted bipartite graph $G$ and a constraint $k$, we aim to find the maximum $k$-balanced biclique (Max $k$ BB) with the maximum weight. To address the problem, we first propose an approach based on biclique enumeration on single side of $G$ following the Branch-and-Bound framework. To improve the performance, we further devise three optimization strategies to prune invalid search branches. Moreover, we utilize graph reduction strategy to reduce the redundant search space. Extensive experiments are conducted on 12 real bipartite datasets to demonstrate the efficiency, effectiveness and scalability of our proposed algorithms. The experimental results show that our algorithms can address MBB detection problem efficiently, and the case study demonstrates the effectiveness of our model compared with MBB model.

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