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Observability transitions in clustered networks
- Publication Year :
- 2018
-
Abstract
- We investigate the effect of clustering on network observability transitions. In the observability model introduced by Yang, Wang, and Motter [Phys. Rev. Lett. 109, 258701 (2012)], a given fraction of nodes are chosen randomly, and they and those neighbors are considered to be observable, while the other nodes are unobservable. For the observability model on random clustered networks, we derive the normalized sizes of the largest observable component (LOC) and largest unobservable component (LUC). Considering the case where the numbers of edges and triangles of each node are given by the Poisson distribution, we find that both LOC and LUC are affected by the network's clustering: more highly-clustered networks have lower critical node fractions for forming macroscopic LOC and LUC, but this effect is small, becoming almost negligible unless the average degree is small. We also evaluate bounds for these critical points to confirm clustering's weak or negligible effect on the network observability transition. The accuracy of our analytical treatment is confirmed by Monte Carlo simulations.<br />12 pages, 6 figures
- Subjects :
- Social and Information Networks (cs.SI)
FOS: Computer and information sciences
Statistics and Probability
Physics - Physics and Society
Degree (graph theory)
Node (networking)
Monte Carlo method
FOS: Physical sciences
Statistical and Nonlinear Physics
Observable
Computer Science - Social and Information Networks
Physics and Society (physics.soc-ph)
Poisson distribution
01 natural sciences
Unobservable
010305 fluids & plasmas
symbols.namesake
0103 physical sciences
symbols
Statistical physics
Observability
010306 general physics
Cluster analysis
Mathematics
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....68be1bbea6db58ee9fb21c42664ba442