Local expansion methods excel in efficiency for mining overlapping communities in real-world networks. However, two problems prevent such methods from identifying diversely structured communities. First, local expansion methods generate independent communities only. Second, local expansion methods depend heavily on quality functions. This work provides a solution for local expansion methods to identify diversely structured communities. The proposed overlapping community detection algorithm performs local expansion and boundary re-checking sub-processes in order. The local expansion process first gets a cover of the network, and then the boundary re-checking process optimizes the cover of the network resulting from the local expansion process. To solve the first problem, the proposed algorithm establishes associations between boundaries of adjacent communities via the boundary re-checking process. To solve the second problem, the proposed algorithm expands and optimizes communities based on node-community membership optimization. We compared the proposed algorithm to seven state-of-the-art algorithms by examining their performance on five groups of artificial networks and sixteen real-world networks. Experimental results showed that the proposed algorithm outperforms compared algorithms in identifying diversely structured communities.

本地扩展方法在挖掘真实网络中重叠社区的效率方面表现出色。但是，有两个问题阻止了这种方法识别结构多样的社区。首先，本地扩张方法只能产生独立的社区。其次，本地扩展方法在很大程度上取决于质量功能。这项工作为本地扩展方法提供了解决方案，以识别结构多样的社区。所提出的重叠社区检测算法按顺序执行局部扩展和边界重新检查子过程。本地扩展过程首先获得网络的覆盖范围，然后边界重新检查过程优化由本地扩展过程产生的网络覆盖范围。为了解决第一个问题，该算法通过边界重新检查过程在相邻社区的边界之间建立关联。为了解决第二个问题，该算法基于节点社区成员资格优化对社区进行了扩展和优化。通过检查它们在五组人工网络和十六个现实网络中的性能，我们将提出的算法与七个最新算法进行了比较。实验结果表明，该算法在识别不同结构的社区方面优于算法。通过检查它们在五组人工网络和十六个现实网络中的性能，我们将提出的算法与七个最新算法进行了比较。实验结果表明，该算法在识别不同结构的社区方面优于算法。通过检查它们在五组人工网络和十六个现实网络中的性能，我们将提出的算法与七个最新算法进行了比较。实验结果表明，该算法在识别不同结构的社区方面优于算法。