Optimized evolution of networks for principal eigenvector localization

Abstract

Network science is increasingly being developed to get new insights about behavior and properties of complex systems represented in terms of nodes and interactions. One useful approach is investigating localization properties of eigenvectors having diverse applications including disease-spreading phenomena in underlying networks. In this work, we evolve an initial random network with an edge rewiring optimization technique considering the inverse participation ratio as a fitness function. The evolution process yields a network having localized principal eigenvector. We analyze various properties of the optimized networks and those obtained at the intermediate stage. Our investigations reveal the existence of few special structural features of such optimized networks including the presence of a set of edges which are necessary for the localization, and rewiring only one of them leads to a complete delocalization of the principal eigenvector. Our investigation reveals that PEV localization is not a consequence of a single network property, and preferably requires co-existence of various distinct structural as well as spectral features.

arXiv comment7 pages, and 8 figures

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Cited by in corpus (10)

• Spectral properties of complex networks
• Discrete-time quantum walk on complex networks for community detection
• Principal eigenvector localization and centrality in networks: revisited
• Localization of multilayer networks by the optimized single-layer rewiring
• Engineering chimera patterns in networks using heterogeneous delays
• Functional control of network dynamics using designed Laplacian spectra
• From Spectra to Localized Networks: A Reverse Engineering Approach
• Wheel graph strategy for PEV localization of networks
• Explosive Synchronization in Multilayer Dynamically Dissimilar Networks
• Network construction: A learning framework through localizing principal eigenvector