The mixing matrix of a graph gives the density of edges between vertices with different characteristics. The mixing matrix for a given igraph object can be calculated using the following function:
The assortativity coefficient, based on Newman’s paper, can be calculated from the mixing matrix by the following:
Here is an example (be sure to load the functions mentioned above):
Cool.
Nice!
However, I’ve noticed that your code does not distinguish between directed and undirected graphs and does not produce eij =eji (it seems that the program always considers first node in the edge list as a source and the second node as a target).
This part should fix it (and it also should run bit faster)
for (i in 1:numatts)
for (j in 1:numatts)
{
iNode <- V(mygraph)[attrib == attlist[i]]
jNode <- V(mygraph)[attrib == attlist[j]]
if (is.directed(mygraph))
mm[i, j] %jNode])
else
mm[i, j] <- length(E(mygraph)[iNode%--%jNode])
}
Vessy,
nice add, but could you please check the code you posted? looks like it is meaningless in:
mm[i, j] %jNode])
did you mean:
mm[i,j] %jNode])
best,
Simone
ok, I see, it’s the posting thing that strips out the code… let’s try this
mm[i,j] <- length(E(mygraph)[iNode%-&%jNode])
Is there a standard way (and syntax) to assess the significance of the assortativity coefficient for a given graph?