Make a Graph Stack Row, Column, or Row-column Stochastic
Returns a graph stack in which each adjacency matrix in dat has been normalized to row stochastic, column stochastic, or row-column stochastic form, as specified by mode.
make.stochastic(dat, mode="rowcol", tol=0.005,
maxiter=prod(dim(dat)) * 100, anneal.decay=0.01, errpow=1)dat |
a collection of input graphs. |
mode |
one of “row,” “col,” or “rowcol”. |
tol |
tolerance parameter for the row-column normalization algorithm. |
maxiter |
maximum iterations for the rwo-column normalization algorithm. |
anneal.decay |
probability decay factor for the row-column annealer. |
errpow |
power to which absolute row-column normalization errors should be raised for the annealer (i.e., the penalty function). |
Row and column stochastic matrices are those whose rows and columns sum to 1 (respectively). These are quite straightforwardly produced here by dividing each row (or column) by its sum. Row-column stochastic matrices, by contrast, are those in which each row and each column sums to 1. Here, we try to produce row-column stochastic matrices whose values are as close in proportion to the original data as possible by means of an annealing algorithm. This is probably not optimal in the long term, but the results seem to be consistent where row-column stochasticization of the original data is possible (which it is not in all cases).
The stochasticized adjacency matrices
Rows or columns which sum to 0 in the original data will generate undefined results. This can happen if, for instance, your input graphs contain in- or out-isolates.
Carter T. Butts buttsc@uci.edu
#Generate a test matrix g<-rgraph(15) #Make it row stochastic make.stochastic(g,mode="row") #Make it column stochastic make.stochastic(g,mode="col") #(Try to) make it row-column stochastic make.stochastic(g,mode="rowcol")
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.