gs.edge.log: Run pass-to-log on a weighted graph.
In neurodata/graphstats: Graph Statistics

Extracts edge weight vector W from a graph and replaces it with log(W + eps) where log is the logarithm operation.

1 2	gs.edge.log(g, edge.attr = NULL, base = exp(1), eps = 1, output.type = "matrix")

`g`	an `igraph` object or an `nxn` adjacency matrix with `n` vertices.
`edge.attr`	if `g` is a `igraph`, the name of the attribute to use for weights. Defaults to `NULL`, which assumes the graph is binary. `is.null(edge.attr)` constructs sbm on the graph as a binary adjacency matrix. `is.character(edge.attr)` constructs sbm of the graph the graph as a weighted adjacency matrix, with edge-weights for `E(g)` given by `get.edge.attribute(g, attr=edge.attr)`.
`base`	the base for the logarithm operation. Defaults to base `exp(1)`, or the natural logarithm. Options are real numbers greater than `0`.
`eps`	the offset for taking the logarithm, in the event that there are entries of `g` that are less than or equal to `0`.
`output.type`	the type of output to produce for the between community expectations. Defaults to `matrix`. `"matrix"` produces a matrix for the between-communitity interactions. `"graph"` produces an `igraph` object for the between-community interactions.