ParallelHeatrank is a wrapper that computes heatranks for (possibly)
different backgrounds and for multiple inputs at once. It will
reuse the permutations, which have to be passed to the function.
The input must be binary for this implementation, so numeric values for
each node are not supported.
ParallelHeatrank(R, perm, G)
dense matrix with the diffusion kernel
dense matrix with the permutations (indices in columns).
This has to ensure that enough indices are sampled, i.e. at least as
great as the largest list in the input (largest
S4 sparse matrix with the heat sources
a matrix with the same amount of rows that
and columns in
G, containing the heatrank scores. These scores
are corrected using
(r + 1)/(p + 1)
r/p. The smaller the score, the
warmer the node.
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