Network coherence on the weighted treelike network

Yue Zong, M. Dai, Jiaojiao He, Xiaoqian Wang
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Abstract

The study of complex networks has gained much interest. In particular, network coherence is a current hot topic. In this paper, we construct the weighted treelike network with weight factor to be our model. Although some properties have been revealed in weighted treelike networks, studies on their network coherence are still less and remain a challenge. In order to enrich the research of weighted networks, the first-order network coherence is investigated in this paper. We investigates consensus dynamics in a linear dynamical system with additive stochastic disturbances, which is characterized as network coherence by the Laplacian spectrum. Based on the tree structures, we identify a recursive relationship of its eigenvalues at two successive generations of Laplacian matrix. We then compute and derive the exact solutions for the sum of reciprocals of all nonzero Laplacian eigenvalues. The obtained results show that the scalings of first-order coherence with network size obey three laws along with the range of the weight factor. All results in this paper can help us get deeper understanding about the effect of link weight on the properties and functions of networks.
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加权树状网络的网络相干性
复杂网络的研究引起了人们极大的兴趣。网络相干性是当前研究的热点问题。在本文中,我们构造了带权重因子的加权树状网络作为我们的模型。虽然加权树状网络的一些特性已经被揭示出来,但对其网络相干性的研究仍然很少,仍然是一个挑战。为了丰富加权网络的研究内容,本文对一阶网络相干性进行了研究。研究了具有加性随机扰动的线性动力系统的一致动力学,该系统用拉普拉斯谱表征为网络相干性。基于树形结构,我们确定了其特征值在连续两代拉普拉斯矩阵上的递归关系。然后,我们计算并推导出所有非零拉普拉斯特征值的倒数和的精确解。结果表明,一阶相干性随网络大小的缩放随权重因子范围的变化遵循三个规律。本文的所有结果都有助于我们更深入地理解链路权重对网络属性和功能的影响。
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