IEEE Trans Cybern. 2021 Jun;51(6):3348-3360. doi: 10.1109/TCYB.2019.2933041. Epub 2021 May 18.
The local community detection is a significant branch of the community detection problems. It aims at finding the local community to which a given starting node belongs. The local community detection plays an important role in analyzing the complex networks and recently has drawn much attention from the researchers. In the past few years, several local community detection algorithms have been proposed. However, the previous methods only make use of the limited local information of networks but overlook the other valuable information. In this article, we propose an evolutionary computation-based algorithm called evolutionary-based local community detection (ELCD) algorithm to detect local communities in the complex networks by taking advantages of the entire obtained information. The performance of the proposed algorithm is evaluated on both synthetic and real-world benchmark networks. The experimental results show that the proposed algorithm has a superior performance compared with the state-of-the-art local community detection methods. Furthermore, we test the proposed algorithm on incomplete real-world networks to show its effectiveness on the networks whose global information cannot be obtained.
本地社区检测是社区检测问题的一个重要分支。它旨在找到给定起始节点所属的本地社区。本地社区检测在分析复杂网络方面起着重要作用，最近引起了研究人员的广泛关注。在过去的几年中，已经提出了几种本地社区检测算法。然而，以前的方法仅利用网络的有限本地信息，而忽略了其他有价值的信息。在本文中，我们提出了一种基于进化计算的算法，称为基于进化的本地社区检测（ELCD）算法，通过利用获得的全部信息来检测复杂网络中的本地社区。我们在合成和真实世界基准网络上评估了所提出算法的性能。实验结果表明，与最先进的本地社区检测方法相比，所提出的算法具有更好的性能。此外，我们在不完整的真实世界网络上测试了所提出的算法，以展示其在无法获得全局信息的网络上的有效性。
