AI 中文总结
研究物理网络中扩散动力学的定位转变，核心方法是求解分析模型，发现度-体积比为无序参数，其失谐重组拉普拉斯谱，揭示了特征丰富驱动的定位控制机制，表明物理特性重塑网络动力学无序格局。
AI 中文摘要
网络扩散是许多传输现象的基础，拉普拉斯模式决定信息传播和弛豫方式。然而在物理网络中，仅连接性不足，节点体积会引入局部停留时间来调节流量传播前的存储方式。本文表明物理异质性重塑拓扑驱动的定位，度-体积比成为相关无序参数。我们求解了一个分析模型，其中比率失谐定性地重组拉普拉斯谱，并在实证网络中证明度-体积相关性如何使极值本征模远离仅由拓扑选择的节点。结果揭示了一种通用的特征丰富驱动的定位控制机制，表明物理特性非平凡地重塑了控制网络动力学的无序格局。
英文摘要
Network diffusion underlies many transport phenomena, with Laplacian modes setting how information spreads and relaxes. In physical networks, however, connectivity alone is not enough: node volumes introduce local dwell times that regulate how flow is stored before being propagated. Here we show that physical heterogeneity reshapes topology-driven localization, with the degree-volume ratio emerging as the relevant disorder parameter. We solve an analytical model in which ratio detuning qualitatively reorganizes the Laplacian spectrum, and demonstrate in empirical networks how degree-volume correlations shift extremal eigenmodes away from the nodes selected by topology alone. Our results reveal a general feature-rich-driven mechanism for localization control, showing that physicality non-trivially reshapes the disorder landscape governing network dynamics.
Comments6 pages, 3 figures
