topology: Network topology analysis
In ProNet: Biological Network Construction, Visualization and Analyses

Description Usage Arguments Value References Examples

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

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)

`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`).

A list of topological parameters and plots.

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.

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)

Loading required package: Rcpp
Loading required package: igraph

Attaching package: 'igraph'

The following objects are masked from 'package:stats':

    decompose, spectrum

The following object is masked from 'package:base':

    union

Loading required package: MCL
Loading required package: linkcomm
Loading required package: RColorBrewer

Welcome to linkcomm version 1.0-11

For a step-by-step guide to using linkcomm functions:
   > vignette(topic = "linkcomm", package = "linkcomm")
To run an interactive demo:
   > demo(topic = "linkcomm", package = "linkcomm")
To cite, see:
   > citation("linkcomm")
NOTE: To use linkcomm, you require read and write permissions in the current directory (see: help("getwd"), help("setwd"))

Simple statistics of the network:
 Number of nodes :  86 ;
 Number of edges :  276 ;
 Connected components :  1 ;
 Isolated nodes :  0 ;
 Number of self-loops :  72 ;
 Average number of neighbors :  4.651163 ;
 Average path length :  2.453352 ;
 Network diameter :  4 ;
 Density :  0.075513 ;
 Cluster coefficient :  0.1193634 ;
Simple statistics of the network:
 Number of nodes :  86 ;
 Number of edges :  276 ;
 Connected components :  1 ;
 Isolated nodes :  0 ;
 Number of self-loops :  72 ;
 Average number of neighbors :  4.651163 ;
 Average path length :  2.453352 ;
 Network diameter :  4 ;
 Density :  0.075513 ;
 Cluster coefficient :  0.1193634 ;