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首页> 外文期刊>Physica, A. Statistical mechanics and its applications >Detecting community structure from coherent oscillation of excitable systems
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Detecting community structure from coherent oscillation of excitable systems

机译：从可激发系统的相干振荡中检测群落结构

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Many networks are proved to have Community structures. Oil the basis of the fact that the dynamics oil networks are intensively affected by the related topology, in this paper the dynamics of excitable systems oil networks and a corresponding approach for detecting communities are discussed. Dynamical networks are formed by interacting neurons; each neuron is described using the FHN model. For noisy disturbance and appropriate coupling strength, neurons may oscillate coherently and their behavior is tightly related to the community structure. Synchronization between nodes is measured in terms of a correlation coefficient based on long time series. The correlation coefficient matrix call be used to project network topology onto a vector space. Then by the K-means cluster method, the communities can be detected. Experiments demonstrate that our algorithm is effective at discovering community structure in artificial networks and real networks, especially for directed networks. The results also provide us with a deep understanding of the relationship Of function and structure for dynamical networks.
机译：事实证明，许多网络都具有社区结构。石油是基于动态油网受相关拓扑强烈影响这一事实的基础，本文讨论了可激发系统油网的动力学以及相应的社区探测方法。动力网络是由相互作用的神经元形成的。使用FHN模型描述每个神经元。对于嘈杂的干扰和适当的耦合强度，神经元可能会连贯地振荡，并且其行为与社区结构紧密相关。节点之间的同步是根据基于长时间序列的相关系数来衡量的。相关系数矩阵调用用于将网络拓扑投影到向量空间上。然后，通过K-means聚类方法，可以检测到社区。实验表明，我们的算法可以有效地发现人工网络和实际网络中的社区结构，尤其是定向网络。结果还使我们对动态网络的功能和结构之间的关系有了更深入的了解。

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