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

## Description

Calculate the effective diameter of a graph.

## Usage

 ```1 2 3 4 5 6 7 8``` ```metric.distance.effdia( Network, probability = 0.95, error = 0.03, effective_rate = 0.9, Cores = detectCores(), full = TRUE ) ```

## Arguments

 `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.

## Details

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.

## Author(s)

Luis Castro, Nazrul Shaikh.

## References

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.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```## 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) ```

fastnet documentation built on Jan. 13, 2021, 5:28 p.m.