Maximum Biplex Search over Bipartite Graphs

• Published in: 2022 IEEE 38th International Conference on Data Engineering (ICDE) pp. 898 - 910
• Main Authors: Luo, Wensheng, Li, Kenli, Zhou, Xu, Gao, Yunjun, Li, Keqin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2022
• Subjects: Bipartite graph; Conferences; Data engineering; Graph query; Heuristic algorithms; Image edge detection; k-biplex; Parallel algorithm; Scalability; Search problems; Subgraph search
• ISSN: 2375-026X
• DOI: 10.1109/ICDE53745.2022.00072

As a typical most-to-most connected quasi-biclique model, k-biplex is a superset of bicliques, which allows nodes on each side of a fully connected subgraph to lose at most k connections. In this paper, we investigate the maximum biplex search problem for the first time. The goal here is to find a k-biplex with the maximum number of edges and we have proved that the problem is NP-hard. It is widely used in fraudulent reviewer group detection, gene expression analysis, social recommendation, and other real-life applications. To solve this problem, a maximum k-biplex search algorithm (MBS) is first presented by integrating two pruning strategies, including degree-based and 2-hop-based pruning. In addition, we define a new dense subgraph over bipartite graphs, \langle x, y\rangle -core, and develop a core-based maximum k-biplex search algorithm (MBS-Core) which can significantly reduce the search space with the introduction of a core-based graph reduction technique. In particular, it only needs to search these cores instead of the entire graph to obtain the maximum k-biplex. Moreover, a parallel algorithm and a heuristic algorithm are developed to achieve better query performance on larger-scale bipartite graphs. Extensive experiments have been conducted on real-life and synthetic datasets to verify the efficiency and effectiveness of the proposed algorithms. Our results show that MBS-Core is up to 3 orders of magnitude faster than the existing approaches.