论文标题
非均质网络的拉普拉斯重归其化组
Laplacian Renormalization Group for heterogeneous networks
论文作者
论文摘要
重新归一化群体是现代通用和相变理论的基石，它是在动态系统中仔细检查对称性和组织量表的强大工具。但是，由于相互交织的量表之间的相关性，其网络对应物特别具有挑战性。迄今为止，探索基于隐藏的几何假设。在这里，我们在复杂的网络中提出了基于拉普拉斯RG扩散的图像，它既定义了Kadanoff Supernodes的概念，即动量空间过程，\ Emph {Álala Wilson}，并将此RG方案应用于自然和偏见的方式。
The renormalization group is the cornerstone of the modern theory of universality and phase transitions, a powerful tool to scrutinize symmetries and organizational scales in dynamical systems. However, its network counterpart is particularly challenging due to correlations between intertwined scales. To date, the explorations are based on hidden geometries hypotheses. Here, we propose a Laplacian RG diffusion-based picture in complex networks, defining both the Kadanoff supernodes' concept, the momentum space procedure, \emph{á la Wilson}, and applying this RG scheme to real networks in a natural and parsimonious way.
