@Robinlovelace That gets us to this:

library(dodgr)
graph <- weight_streetnet (hampi, wt_profile = "foot")
igr <- dodgr_to_igraph (graph)
graph_c <- dodgr_contract_graph (graph)
igr_c <- dodgr_to_igraph (graph_c)

# edge betweenness on full graph
f_dodgr <- function (graph) dodgr_centrality (graph, contract = FALSE, edges = TRUE)
f_igraph <- function (graph) igraph::edge_betweenness (graph)
rbenchmark::benchmark (
             res_d <- f_dodgr (graph),
             res_i <- f_igraph (igr),
             replications = 2
) [, 1:4]
#>                      test replications elapsed relative
#> 1 res_d <- f_dodgr(graph)            2   1.234    1.213
#> 2  res_i <- f_igraph(igr)            2   1.017    1.000
identical (res_i, res_d$centrality)
#> [1] TRUE

# edge betweenness on contracted graph
rbenchmark::benchmark (
             res_d <- f_dodgr (graph_c),
             res_i <- f_igraph (igr_c),
             replications = 2
) [, 1:4]
#>                        test replications elapsed relative
#> 1 res_d <- f_dodgr(graph_c)            2   0.022    1.222
#> 2  res_i <- f_igraph(igr_c)            2   0.018    1.000
identical (res_i, res_d$centrality)
#> [1] TRUE

^{Created on 2019-10-16 by the reprex package (v0.3.0)}

dodgr is only about 20% slower, essentially because it's C++ while igraph is pure C. But the point of this is now the trivial extra step of wrapping it in RcppParalllel, so that dodgr will effectively be several times faster through parallelisation... Re-opening until that's been done.

network centrality function #90

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