Bipartite graphs, which consist of two different types of entities, are widely used to model many real-world applications. In bipartite networks, (α,β)-core is an essential model to measure the entity engagement. In this paper, we propose and investigate the problem of (α,β)-core minimization, which aims to identify a set of b edges whose deletion can minimize the size of resulting collapsed (α,β)-core. To our best knowledge, this is the first work to investigate the (α,β)-core minimization problem in bipartite graph. We prove the problem is NP-hard and our object function is monotonic but not submodular. Then, we propose a baseline algorithm by invoking the greedy framework. To reduce the computation cost and candidate space, novel pruning techniques are devised. We further develop a group based algorithm to optimize the search. Finally, we conduct comprehensive experiments over 6 real-life bipartite networks to demonstrate the advantages of the proposed techniques.

二部图由两种不同类型的实体组成，广泛用于对许多实际应用程序进行建模。在双向网络中，( α , β )-core 是衡量实体参与度的基本模型。在本文中，我们提出并研究了 ( α , β )-core 最小化问题，旨在确定一组b 条边，其删除可以最小化结果折叠 ( α , β )-core 的大小。据我们所知，这是第一项研究 ( α , β)-二部图中的核心最小化问题。我们证明了这个问题是 NP-hard 问题，我们的目标函数是单调的但不是子模块的。然后，我们通过调用贪婪框架提出了一个基线算法。为了降低计算成本和候选空间，设计了新颖的剪枝技术。我们进一步开发了一个基于组的算法来优化搜索。最后，我们对 6 个现实生活中的二分网络进行了全面的实验，以证明所提出技术的优势。