metric.distance.effdia: Effective Diameter
In networkgroupR/fastnet: Large-Scale Social Network Analysis

Description Usage Arguments Details Value Author(s) References Examples

Calculate the effective diameter of a graph.

1 2	metric.distance.effdia(Network, probability = 0.95, error = 0.03, effective_rate = 0.9, Cores = detectCores(), full = TRUE)

`Network`	The input network.
`probability`	The confidence level probability
`error`	The sampling error
`effective_rate`	The effective rate (by default it is set to be 0.9)
`Cores`	Number of cores to use in the computations. By default uses parallel function `detecCores()`.
`full`	It will calculate the popular full version by default. If it is set to FALSE, the estimated diameter will be calculated.

The diameter is the largest shortest path lengths of all pairs of nodes in graph Network. metric.distance.diameter calculates the (estimated) diameter of graph Network with a justified error.

A real value.

Luis Castro, Nazrul Shaikh.

Dijkstra EW. A note on two problems in connexion with graphs:(numerische mathematik, _1 (1959), p 269-271). 1959.

Castro L, Shaikh N. Estimation of Average Path Lengths of Social Networks via Random Node Pair Sampling. Department of Industrial Engineering, University of Miami. 2016.

## Not run: 
##Default function
x <-  net.erdos.renyi.gnp(1000,0.01)
metric.distance.effdia(x)
##Population APL
metric.distance.effdia(x, full=TRUE)
##Sampling at 99% level with an error of 10% using 5 cores
metric.distance.effdia(Network = x, probability=0.99, error=0.1, Cores=5)

## End(Not run)