Giant Clusters in Random Ad Hoc Networks
arXiv:cond-mat/0211325 · doi:10.1103/PhysRevE.67.036110
Abstract
The present paper introduces ad hoc communication networks as examples of large scale real networks that can be prospected by statistical means. A description of giant cluster formation based on the single parameter of node neighbor numbers is given along with the discussion of some asymptotic aspects of the giant cluster sizes.
6 pages, 5 figures; typos and corrections
References in corpus (14)
- Emergence of scaling in random networks
- The structure of scientific collaboration networks
- The diameter of the world wide web
- The large-scale organization of metabolic networks
- Random graphs with arbitrary degree distributions and their applications
- Classes of behavior of small-world networks
- Resilience of the Internet to random breakdowns
- Mean-field theory for scale-free random networks
- Evolution of the social network of scientific collaborations
- How Popular is Your Paper? An Empirical Study of the Citation Distribution
- Breakdown of the Internet under intentional attack
- Spectra of "Real-World" Graphs: Beyond the Semi-Circle Law
- Small-world networks: Evidence for a crossover picture
- Small Cycles in Small Worlds
Cited by in corpus (13)
- Spatial Networks
- The effects of spatial constraints on the evolution of weighted complex networks
- WiFi Epidemiology: Can Your Neighbors' Router Make Yours Sick?
- Connectivity Distribution of Spatial Networks
- Geographical effects on cascading breakdowns of scale-free networks
- Topological data analysis of continuum percolation with disks
- Epidemic spreading in lattice-embedded scale-free networks
- Epidemic Variability in Hierarchical Geographical Networks with Human Activity Patterns
- Geographical effects on epidemic spreading in scale-free networks
- Enhancing Robustness and Immunization in geographical networks
- Scaling of critical connectivity of mobile ad hoc communication networks
- Complex systems: features, similarity and connectivity
- Transitions in spatial networks