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Community detection in complex networks via adapted Kuramoto dynamics

机译:通过改编的仓本动力学在复杂网络中进行社区检测

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Based on the Kuramoto model, a new network model, namely, the generalized Kuramoto model with Fourier term, is introduced for studying community detection in complex networks. In particular, the Fourier term provides a natural phase locking of the trajectories into a pre-defined number of clusters. A mathematical approach is used to study the behavior of the solutions and its properties. Conditions for properly choosing the coupling parameters so that phase locking takes place are presented and a quality function called clustering density is introduced to measure the effectiveness of the communities identification. Illustrations with real and synthetic networks with community structure are presented. (C) 2017 Elsevier B.V. All rights reserved.
机译:在Kuramoto模型的基础上,引入了一种新的网络模型,即带有Fourier项的广义Kuramoto模型,用于研究复杂网络中的社区检测。特别地,傅立叶项提供了将轨迹自然锁相到预定数量的簇中。使用数学方法来研究溶液的行为及其性质。给出了正确选择耦合参数以便进行锁相的条件,并引入了一种称为聚类密度的质量函数来衡量社区识别的有效性。提出了具有社区结构的真实和合成网络的插图。 (C)2017 Elsevier B.V.保留所有权利。

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