Department of Physics, Complex systems Lab, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore-453552, India.
Phys Rev E. 2023 Mar;107(3-1):034311. doi: 10.1103/PhysRevE.107.034311.
Localization behaviors of Laplacian eigenvectors of complex networks furnish an explanation to various dynamical phenomena of the corresponding complex systems. We numerically examine roles of higher-order and pairwise links in driving eigenvector localization of hypergraphs Laplacians. We find that pairwise interactions can engender localization of eigenvectors corresponding to small eigenvalues for some cases, whereas higher-order interactions, even being much much less than the pairwise links, keep steering localization of the eigenvectors corresponding to larger eigenvalues for all the cases considered here. These results will be advantageous to comprehend dynamical phenomena, such as diffusion, and random walks on a range of real-world complex systems having higher-order interactions in better manner.
复杂网络的拉普拉斯特征向量的局域化行为为相应复杂系统的各种动力学现象提供了一种解释。我们数值研究了高阶和成对链路在驱动超图拉普拉斯特征向量局域化中的作用。我们发现,对于某些情况,成对相互作用可以导致小特征值对应的特征向量局域化,而高阶相互作用,即使比成对链路少得多,对于这里考虑的所有情况,仍然可以保持对应较大特征值的特征向量的局域化。这些结果将有利于更好地理解扩散等动力学现象,以及在具有高阶相互作用的一系列真实复杂系统上的随机漫步。