标题： 拉普拉斯特征向量中心性 显示英文标题

标题： Laplacian Eigenvector Centrality

摘要： 网络显著影响社会、经济和组织结果，中心性度量作为捕捉个体节点重要性的关键工具。本文介绍了拉普拉斯特征向量中心性（LEC），这是一种基于谱图理论和拉普拉斯矩阵特征分解的网络分析新框架。LEC的一个显著特点是其可调参数，即LEC阶数，这使研究人员能够通过拉普拉斯谱控制和评估中心性测量的范围。使用随机图模型，LEC在各种网络结构中表现出鲁棒性和可扩展性。我们将LEC与经济模型中对外部冲击的均衡响应联系起来，展示了LEC如何通过二次优化量化代理在减弱冲击和促进协调响应中的作用。最后，我们将LEC应用于微金融扩散的研究，说明它如何通过捕捉节点在网络中独特的位置方面，补充了经典的中心性度量，如特征向量和Katz-Bonacich中心性。 显示英文摘要

摘要： Networks significantly influence social, economic, and organizational outcomes, with centrality measures serving as crucial tools to capture the importance of individual nodes. This paper introduces Laplacian Eigenvector Centrality (LEC), a novel framework for network analysis based on spectral graph theory and the eigendecomposition of the Laplacian matrix. A distinctive feature of LEC is its adjustable parameter, the LEC order, which enables researchers to control and assess the scope of centrality measurement using the Laplacian spectrum. Using random graph models, LEC demonstrates robustness and scalability across diverse network structures. We connect LEC to equilibrium responses to external shocks in an economic model, showing how LEC quantifies agents' roles in attenuating shocks and facilitating coordinated responses through quadratic optimization. Finally, we apply LEC to the study of microfinance diffusion, illustrating how it complements classical centrality measures, such as eigenvector and Katz-Bonacich centralities, by capturing distinctive aspects of node positions within the network.