Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 2 de 2
Filter
Add more filters










Database
Language
Publication year range
1.
Nat Commun ; 10(1): 1017, 2019 03 04.
Article in English | MEDLINE | ID: mdl-30833554

ABSTRACT

Real-world networks are often claimed to be scale free, meaning that the fraction of nodes with degree k follows a power law k-α, a pattern with broad implications for the structure and dynamics of complex systems. However, the universality of scale-free networks remains controversial. Here, we organize different definitions of scale-free networks and construct a severe test of their empirical prevalence using state-of-the-art statistical tools applied to nearly 1000 social, biological, technological, transportation, and information networks. Across these networks, we find robust evidence that strongly scale-free structure is empirically rare, while for most networks, log-normal distributions fit the data as well or better than power laws. Furthermore, social networks are at best weakly scale free, while a handful of technological and biological networks appear strongly scale free. These findings highlight the structural diversity of real-world networks and the need for new theoretical explanations of these non-scale-free patterns.


Subject(s)
Information Services/classification , Models, Statistical , Models, Theoretical , Computer Simulation , Humans , Models, Biological , Social Networking
2.
Math Biosci Eng ; 9(3): 487-526, 2012 Jul.
Article in English | MEDLINE | ID: mdl-22881023

ABSTRACT

In this paper, we investigate three particular algorithms: a stochastic simulation algorithm (SSA), and explicit and implicit tau-leaping algorithms. To compare these methods, we used them to analyze two infection models: a Vancomycin-resistant enterococcus (VRE) infection model at the population level, and a Human Immunodeficiency Virus (HIV) within host infection model. While the first has a low species count and few transitions, the second is more complex with a comparable number of species involved. The relative efficiency of each algorithm is determined based on computational time and degree of precision required. The numerical results suggest that all three algorithms have the similar computational efficiency for the simpler VRE model, and the SSA is the best choice due to its simplicity and accuracy. In addition, we have found that with the larger and more complex HIV model, implementation and modification of tau-Leaping methods are preferred.


Subject(s)
Algorithms , Gram-Positive Bacterial Infections/epidemiology , Gram-Positive Bacterial Infections/transmission , HIV Infections/epidemiology , HIV Infections/transmission , Models, Statistical , Computer Simulation/statistics & numerical data , Enterococcus/drug effects , Gram-Positive Bacterial Infections/drug therapy , Humans , Population Dynamics , Vancomycin Resistance
SELECTION OF CITATIONS
SEARCH DETAIL
...