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A Community Structure Enhancement-Based Community Detection Algorithm for Complex Networks

机译:基于社区结构增强的复杂网络社区检测算法

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摘要

Community detection has been recognized as one of the most important tools to discover useful information hidden in complex networks which is usually hard to be obtained by simple observations. Existing community detection algorithms have demonstrated their effectiveness on a variety of complex networks, most of them, however, suffer from the scalability issue on complex networks without a clear community structure due to the challenge in the detection of ambiguous community structure. To address this issue, in this paper, we propose a community structure enhancement method, termed CSE, for community detection in complex networks. In the proposed CSE, the community structure of a network is enhanced by adding links between the nodes possibly belonging to the same community and reducing links between those belonging to different communities, thereby converting an ambiguous community structure into a structure much clearer than the original one. The experimental results show the superior performance of the proposed CSE over five state-of-the-art community detection algorithms on both synthetic benchmark networks and real-world networks, especially for those without a clear community structure.
机译:社区检测已被认为是发现隐藏在复杂网络中的有用信息的最重要的工具之一,这通常很难通过简单的观察来获得。现有的社区检测算法已经证明了它们对各种复杂网络的有效性,然而,由于在检测模糊群落结构的挑战中,大多数情况下,他们的大多数情况都遭受了复杂网络上的可扩展性问题而没有明确的社区结构。为了解决这个问题,在本文中,我们提出了一个社区结构增强方法,被称为CSE,用于复杂网络中的社区检测。在所提出的CSE中,通过在可能属于同一社区之间的节点之间添加链接并减少属于不同社区的节点之间的链路来增强网络的社区结构,从而将模糊的群落结构转换为比原始的结构更清晰地更清晰。实验结果表明,拟议的CSE在合成基准网络和现实网络上的五个最先进的社区检测算法上的优越性,特别是对于那些没有明确社区结构的人。

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