scholarly journals When is an ecological network complex? Connectance drives degree distribution and emerging network properties

PeerJ ◽  
2014 ◽  
Vol 2 ◽  
pp. e251 ◽  
Author(s):  
Timothée Poisot ◽  
Dominique Gravel
Keyword(s):  
Degree Distribution ◽  
Ecological Network ◽  
Network Properties ◽  
Network Complex

scholarly journals When is an ecological network complex? Connectance drives degree distribution and emerging network properties

2013 ◽  
Author(s):  
Timothée Poisot ◽  
Dominique Gravel
Keyword(s):  
Network Structure ◽  
Degree Distribution ◽  
Null Model ◽  
Random Networks ◽  
Ecological Network ◽  
Ecosystem Properties ◽  
Network Properties ◽  
Statistical Argument ◽  
Simple Network ◽  
Network Complex

Connectance and degree distributions are important components of the structure of ecological networks. In this contribution, we use a statistical argument and simple network generating models to show that properties of the degree distribution are driven by network connectance. We discuss the consequences of this finding for (1) the generation of random networks in null-model analyses, and (2) the interpretation of network structure and ecosystem properties in relationship with degree distribution.

scholarly journals When is an ecological network complex? Connectance drives degree distribution and emerging network properties

2013 ◽  
Author(s):  
Timothée Poisot ◽  
Dominique Gravel
Keyword(s):  
Network Structure ◽  
Degree Distribution ◽  
Null Model ◽  
Random Networks ◽  
Ecological Network ◽  
Ecosystem Properties ◽  
Network Properties ◽  
Statistical Argument ◽  
Simple Network ◽  
Network Complex

Connectance and degree distributions are important components of the structure of ecological networks. In this contribution, we use a statistical argument and simple network generating models to show that properties of the degree distribution are driven by network connectance. We discuss the consequences of this finding for (1) the generation of random networks in null-model analyses, and (2) the interpretation of network structure and ecosystem properties in relationship with degree distribution.

scholarly journals When is a network complex? Connectance drives degree distribution and emerging network properties

2013 ◽  
Author(s):  
Timothée Poisot ◽  
Dominique Gravel
Keyword(s):  
Network Structure ◽  
Degree Distribution ◽  
Null Model ◽  
Ecological Networks ◽  
Random Networks ◽  
Ecosystem Properties ◽  
Network Properties ◽  
Statistical Argument ◽  
Simple Network ◽  
Network Complex

Connectance and degree distributions are important components of the structure of ecological networks. In this contribution, we use a statistical argument and simple network generating models to show that properties of the degree distribution are driven by network connectance. We discuss the consequences of this finding for (1) the generation of random networks in null-model analyses, and (2) the interpretation of network structure and ecosystem properties in relationship with degree distribution.

scholarly journals When is a network complex? Connectance drives degree distribution and emerging network properties

2013 ◽  
Author(s):  
Timothée Poisot ◽  
Dominique Gravel
Keyword(s):  
Network Structure ◽  
Degree Distribution ◽  
Null Model ◽  
Ecological Networks ◽  
Random Networks ◽  
Ecosystem Properties ◽  
Network Properties ◽  
Statistical Argument ◽  
Simple Network ◽  
Network Complex

Connectance and degree distributions are important components of the structure of ecological networks. In this contribution, we use a statistical argument and simple network generating models to show that properties of the degree distribution are driven by network connectance. We discuss the consequences of this finding for (1) the generation of random networks in null-model analyses, and (2) the interpretation of network structure and ecosystem properties in relationship with degree distribution.

scholarly journals When is a network complex? Connectance drives degree distribution and emerging network properties

2013 ◽  
Author(s):  
Timothée Poisot ◽  
Dominique Gravel
Keyword(s):  
Network Structure ◽  
Degree Distribution ◽  
Null Model ◽  
Ecological Networks ◽  
Random Networks ◽  
Ecosystem Properties ◽  
Network Properties ◽  
Statistical Argument ◽  
Simple Network ◽  
Network Complex

Connectance and degree distributions are important components of the structure of ecological networks. In this contribution, we use a statistical argument and simple network generating models to show that properties of the degree distribution are driven by network connectance. We discuss the consequences of this finding for (1) the generation of random networks in null-model analyses, and (2) the interpretation of network structure and ecosystem properties in relationship with degree distribution.

scholarly journals Using network properties to predict disease dynamics on human contact networks

2011 ◽  
Vol 278 (1724) ◽  
pp. 3544-3550 ◽  
Author(s):  
Gregory M. Ames ◽  
Dylan B. George ◽  
Christian P. Hampson ◽  
Andrew R. Kanarek ◽  
Cayla D. McBee ◽  
...  
Keyword(s):  
Degree Distribution ◽  
Stochastic Simulations ◽  
Disease Spread ◽  
Contact Structures ◽  
Contact Network ◽  
Contact Networks ◽  
Human Contact ◽  
Differential Equation Models ◽  
Network Properties ◽  
Intermediate Density

Recent studies have increasingly turned to graph theory to model more realistic contact structures that characterize disease spread. Because of the computational demands of these methods, many researchers have sought to use measures of network structure to modify analytically tractable differential equation models. Several of these studies have focused on the degree distribution of the contact network as the basis for their modifications. We show that although degree distribution is sufficient to predict disease behaviour on very sparse or very dense human contact networks, for intermediate density networks we must include information on clustering and path length to accurately predict disease behaviour. Using these three metrics, we were able to explain more than 98 per cent of the variation in endemic disease levels in our stochastic simulations.

The study of genetic information flux network properties in genetic algorithms

2015 ◽  
Vol 26 (07) ◽  
pp. 1550076
Author(s):  
Zhengping Wu ◽  
Qiong Xu ◽  
Gaosheng Ni ◽  
Gaoming Yu
Keyword(s):  
Genetic Algorithms ◽  
Empirical Analysis ◽  
Degree Distribution ◽  
Genetic Information ◽  
Selection Pressure ◽  
Selective Pressure ◽  
Node Degree ◽  
Scaling Exponent ◽  
Scale Free ◽  
Network Properties

In this paper, an empirical analysis is done on the information flux network (IFN) statistical properties of genetic algorithms (GA) and the results suggest that the node degree distribution of IFN is scale-free when there is at least some selection pressure, and it has two branches as node degree is small. Increasing crossover, decreasing the mutation rate or decreasing the selective pressure will increase the average node degree, thus leading to the decrease of scaling exponent. These studies will be helpful in understanding the combination and distribution of excellent gene segments of the population in GA evolving, and will be useful in devising an efficient GA.

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