Description Usage Arguments Value Author(s) References See Also Examples
Computes the degree, the minimum path length and the clustering coefficient for a lattice graph.
1 | reg.sw(n.nodes.rand, n.edges.rand, dist.mat)
|
n.nodes.rand |
number of nodes of the graph |
n.edges.rand |
number of edges of the graph |
dist.mat |
matrix with a distance associated to each pair of nodes of the graph to take into account in the computation of the small-world parameters. |
in.degree |
mean of the degree for the whole graph. |
Lp.rand |
mean of the minimum path length for the whole graph. |
Cp.rand |
mean of the clustering coefficient for the whole graph. |
in.degree.all |
vector of the degree of each node of the graph. |
Lp.rand.all |
vector of the minimum path length of each node of the graph. |
Cp.rand.all |
vector of the clustering coefficient of each node of the graph. |
S. Achard
S. H. Strogatz (2001) Exploring complex networks. Nature, Vol. 410, pages 268-276.
S. Achard, R. Salvador, B. Whitcher, J. Suckling, Ed Bullmore (2006) A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs. Journal of Neuroscience, Vol. 26, N. 1, pages 63-72.
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