Observability transitions in correlated networks

Takehisa Hasegawa, Taro Takaguchi, Naoki Masuda

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

Yang, Wang, and Motter analyzed a model for network observability transitions in which a sensor placed on a node makes the node and the adjacent nodes observable. The size of the connected components comprising the observable nodes is a major concern of the model. We analyze this model in random heterogeneous networks with degree correlation. With numerical simulations and analytical arguments based on generating functions, we find that negative degree correlation makes networks more observable. This result holds true both when the sensors are placed on nodes one by one in a random order and when hubs preferentially receive the sensors. Finally, we numerically optimize networks with a fixed degree sequence with respect to the size of the largest observable component. Optimized networks have negative degree correlation induced by the resulting hub-repulsive structure; the largest hubs are rarely connected to each other, in contrast to the rich-club phenomenon of networks.

Original languageEnglish
Article number042809
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume88
Issue number4
DOIs
Publication statusPublished - 2013 Oct 14

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics

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