I have a graph in graph-tool with parallel edges. All parallel edges are
assigned an `EdgePropertyMap` representing their weight with a double. I
would like to return a graph where the edges are condensed into a single
edge where a custom reduce operation on these weights is performed. For
example:
H = gt.Graph(directed=True)
H.add_edge(0,1) # edge 0
H.add_edge(0,1) # edge 1
H.add_edge(0,1) # edge 2
H.add_edge(1,0) # edge 3
H.add_edge(0,2) # edge 4
ew = H.new_edge_property("double")
ew[list(H.edges())[0]]=1.2
ew[list(H.edges())[1]]=2.3
ew[list(H.edges())[2]]=-4.2
ew[list(H.edges())[3]]=5.8
ew[list(H.edges())[3]]=1.0
H.ep['weights'] = ew
In this case the edge (0,1), with a reduce function "sum", should have
total weight 1.2+2.3-4.2= -0.7 while the remaining edges should keep the
same weight (i.e. edge 3 should keep weight 5.8 and edge 4 should maintain
weight 1.0).
Using `gt.condensation_graph` seems to do the trick, but only sum operation
can be performed. To do this we need to use `H.vertex_index` as vertex
property:
Hcond, prop, vcount, ecount, v_avg, edge_avg =
gt.condensation_graph(H,prop=H.vertex_index, aeprops=[H.ep['weights']])
Unfortunately this function only allows sum operation to be performed. What
about using a different "reduce" function, like for example max? What about
operations on non-numeric types of edges?
It would greatly help carrying complex operations directly in Graph-tool,
even if the value type of edges property is different than numeric.
More generally, what about introducing a split-apply-reduce framework to
vertices and edges, or is it already possible with some modifications to
`gt.condensation_graph`?
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