This function will calculate the characteristic path length and clustering coefficient, which are used to calculate small-worldness.
small.world(g, rand)The graph (or list of graphs) of interest
List of (lists of) equivalent random graphs (output from
sim.rand.graph.par)
A data frame with the following components:
The range of density thresholds used.
The number of random graphs that were generated.
The characteristic path length.
The clustering coefficient.
The mean characteristic path length of the random graphs with the same degree distribution as g.
The mean clustering coefficient of the random graphs with the same degree distribution as g.
The normalized characteristic path length.
The normalized clustering coefficient.
The small-world measure of the graph.
Watts D.J., Strogatz S.H. (1998) Collective dynamics of 'small-world' networks. Nature, 393:440-442.