metric.distance.effdia function

Effective Diameter

Calculate the effective diameter of a graph.

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.

Returns

A real value.

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.

Examples

## 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)

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.

Author(s)

Luis Castro, Nazrul Shaikh.