ProNet (version 1.0.0)

topology: Network topology analysis

Description

Calculate the network or graph's topological parameters like degree distribution, clustering coefficient, betweenness, closeness, shortest paths, eigenvector centrality and connectivity.

Usage

topology(graph, simple.parameters = FALSE, degree.distribution = FALSE, power.law = TRUE, fit.line = FALSE, clustering.coefficient = FALSE, betweenness = FALSE, shortest.paths = FALSE, closeness = FALSE, eigenvector.centrality = FALSE, connectivity = FALSE)

Arguments

graph

An igraph object.

simple.parameters

Logical value, indicating whether to do basic statistics (if TRUE) or not (if FALSE).

degree.distribution

Logical value, indicating whether to do degree distribution statistics (if TRUE) or not (if FALSE).

power.law

Logical value, indicating whether the log ratio would be calculated in degree distribution statistics (if TRUE) or not (if FALSE).

fit.line

Logical value, indicating whether to do line fitting in degree distribution statistics (if TRUE) or not (if FALSE).

clustering.coefficient

Logical value, indicating whether to do clustering.coefficient statistics (if TRUE) or not (if FALSE).

betweenness

Logical value, indicating whether to do betweenness statistics (if TRUE) or not (if FALSE).

shortest.paths

Logical value, indicating whether to do shortest.paths statistics (if TRUE) or not (if FALSE).

closeness

Logical value, indicating whether to do closeness statistics (if TRUE) or not (if FALSE).

eigenvector.centrality

Logical value, indicating whether to do eigenvector.centrality statistics (if TRUE) or not (if FALSE).

connectivity

Logical value, indicating whether to do connectivity statistics (if TRUE) or not (if FALSE).

Value

A list of topological parameters and plots.

References

Y Benjamini, Y Hochberg. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological), Vol. 57, No. 1. (1995), pp. 289-300.

Examples

nlocal<-data.frame(c("DVL1","DVL2","DVL3"))
net<-construction(input=nlocal,db="HPRD",species="human",ID.type="Gene symbol",hierarchy=1)
tp<-topology(net,simple.parameters=TRUE)
tp<-topology(net,degree.distribution=TRUE)
tp<-topology(net,simple.parameters=TRUE,degree.distribution=TRUE)