Scaling of weighted spectral distribution in weighted small-world networks

Many real-world systems can be modeled by weighted small-world networks with high clustering coefficients. Recent studies for rigorously analyzing the weighted spectral distribution（W SD） have focused on unweighted networks with low clustering coefficients. In this paper, we rigorously analyze the W SD in a deterministic weighted scale-free small-world network model and find that the W SD grows sublinearly with increasing network order（i.e., the number of nodes） and provides a sensitive discrimination for each input of this model. This study demonstrates that the scaling feature of the W SD exists in the weighted network model which has high and order-independent clustering coefficients and reasonable power-law exponents.

Stability of weighted spectral distribution in a pseudo tree-like network model

The comparison of networks with different orders strongly depends on the stability analysis of graph features in evolving systems. In this paper, we rigorously investigate the stability of the weighted spectral distribution（i.e., a spectral graph feature） as the network order increases. First, we use deterministic scale-free networks generated by a pseudo treelike model to derive the precise formula of the spectral feature, and then analyze the stability of the spectral feature based on the precise formula. Except for the scale-free feature, the pseudo tree-like model exhibits the hierarchical and small-world structures of complex networks. The stability analysis is useful for the classification of networks with different orders and the similarity analysis of networks that may belong to the same evolving system.

新能源风电技术人才成长最优路径研究

新能源风电的可持续发展需要可持续的人才支撑。而风电作为多学科交叉的新兴产业,其技术人才的培养和聚集周期长。因此,如何有效提升风电技术人才成长速度和质量至关重要。本文在对技术人才成长网络特征分析的基础上,利用成长代价函数量化标度不同成长状态关系,构建了风电技术人才加权小世界网络,并通过改进Floyd算法,优化风电技术人才成长路径选择。且通过仿真实验证明这种组合方法有效提高了计算速度和精度,降低了时间复杂度,为有效提升新能源风电技术人才成长速度与质量提供了量化方法支撑。