sp.anogva {statGraph}

Semi-parametric Analysis of Graph Variability (SP-ANOGVA)

Description

sp.anogva statistically tests whether two or more graphs are generated by the same model and set of parameters.

Usage

sp.anogva(Graphs, model, maxBoot = 100, ...)

Arguments

Graphs

a list of undirected graphs. If each graph has the attribute eigenvalues containing its eigenvalues , such values will be used to compute their spectral density.

model

A string that indicates one of the following models: 'ER' (Erdos-Renyi random graph model), 'GRG' (geometric random graph model), 'WS' (Watts-Strogatz random graph model), and 'BA' (Barabási-Albert random graph model).

maxBoot

integer indicating the number of bootstrap resamples (default is 500).

...

Other relevant parameters for graph.param.estimator.

Value

A list with class 'htest' containing the following components:

statistic:

the F statistic of the test.

p.value:

the p-value of the test.

method:

a string indicating the used method.

data.name:

a string with the data's name(s).

estimates:

a vector containing the estimated parameters for each graph.

References

Andre Fujita, Eduardo Silva Lira, Suzana de Siqueira Santos, Silvia Yumi Bando, Gabriela Eleuterio Soares, Daniel Yasumasa Takahashi. A semi-parametric statistical test to compare complex networks, Journal of Complex Networks, cnz028, https://doi.org/10.1093/comnet/cnz028

Sheather, S. J. and Jones, M. C. (1991). A reliable data-based bandwidth selection method for kernel density estimation. _Journal of the Royal Statistical Society series B_, 53, 683-690. http://www.jstor.org/stable/2345597.

Examples

set.seed(1)
model <- 'ER'
G <- list()

# Under H0
G[[1]] <- igraph::sample_gnp(50, 0.5)
G[[2]] <- igraph::sample_gnp(50, 0.5)
G[[3]] <- igraph::sample_gnp(50, 0.5)
result1 <- sp.anogva(G, model, maxBoot = 10,eps=0.1)
result1

# Under H1
G[[1]] <- igraph::sample_gnp(50, 0.5)
G[[2]] <- igraph::sample_gnp(50, 0.75)
G[[3]] <- igraph::sample_gnp(50, 0.5)
result2 <- sp.anogva(G, model, maxBoot = 10,eps=0.1)
result2