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Multipolarization versus unification in community networks

机译:社区网络中的多极化与统一

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Community structure and core–periphery structure are two natural properties of complex networks. Both structures have been studied separately for decades. However, few researchers focus on the combination of these two important structures in complex networks. In this paper, we explore the core–periphery structures of communities in complex networks especially community networks, more precisely, we propose a linear algorithm to divide each community into a densely interconnected core and a periphery where the nodes are rarely linked to each other. Based on core–periphery structures, we perform quantitative analysis of the edges between different communities and find two relationships of two communities in real networks: unitive and multipolar. Communities are called unitive if edges between different cores are more than edges between different peripheries. Otherwise, communities are called multipolar. Furthermore, we propose a random model called Generalized Girvan–Newman(GGN) model, which can generate community networks where communities are either unitive or multipolar. The model sheds some new light on community formation and core–periphery structures in complex systems.
机译:社区结构和核心-外围结构是复杂网络的两个自然属性。两种结构已经分别研究了数十年。但是,很少有研究者关注复杂网络中这两个重要结构的组合。在本文中,我们探索了复杂网络(尤其是社区网络)中社区的核心-外围结构,更准确地说,我们提出了一种线性算法,将每个社区划分为密集互连的核心和节点很少相互链接的外围。基于核心-外围结构,我们对不同社区之间的边缘进行了定量分析,并在实际网络中找到了两个社区的两个关系:统一和多极。如果不同核心之间的边缘大于不同外围之间的边缘,则将社区称为统一的。否则,社区称为多极社区。此外,我们提出了一个称为通用Girvan-Newman(GGN)模型的随机模型,该模型可以生成社区为单一性或多极性的社区网络。该模型为复杂系统中的社区形成和核心-外围结构提供了新的思路。

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