This paper introduces the concepts of spectral influence and spectral cyclicality, both derived from the largest eigenvalue of a graph’s adjacency matrix. These two novel centrality measures capture both diffusion and interdependence from a local and global perspective respectively. We propose a new clustering algorithm that identifies communities with high cyclicality and interdependence, allowing for overlaps. To illustrate our method, we apply it to input-output analysis within the context of the Moroccan economy.

中文翻译：

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网络中的频谱影响：在输入输出分析中的应用

本文介绍了 spectral influence 和 spectral cyclicality 的概念，这两个概念都源自图邻接矩阵的最大特征值。这两个新颖的中心性度量分别从局部和全球的角度捕捉了扩散和相互依存。我们提出了一种新的聚类算法，可以识别具有高周期性和相互依赖性的社区，允许重叠。为了说明我们的方法，我们将其应用于摩洛哥经济背景下的投入产出分析。