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Created August 31, 2016 07:12
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R Igraph Route Performance
> for(i in c(1,10,25,50, 100)){ print(system.time(x <- do_work(300, i, 50)))}
user system elapsed
0.03 0.02 0.08
user system elapsed
0.03 0.02 0.05
user system elapsed
0.04 0.00 0.06
user system elapsed
0.03 0.00 0.05
user system elapsed
0.05 0.02 0.06
Package: igraph
Version: 0.7.1
Date: 2014-04-22
Title: Network analysis and visualization
Author: See AUTHORS file.
Maintainer: Gabor Csardi <[email protected]>
Description: Routines for simple graphs and network analysis. igraph can handle large
graphs very well and provides functions for generating random and regular
graphs, graph visualization, centrality indices and much more.
Depends: methods
Imports: Matrix
Suggests: igraphdata, stats4, rgl, tcltk, graph, ape
License: GPL (>= 2)
URL: http://igraph.org
SystemRequirements: gmp, libxml2
BugReports: https://github.com/igraph/igraph/issues
Packaged: 2014-04-22 21:51:12 UTC; jenkins
Built: R 3.1.0; i386-w64-mingw32; 2014-04-22 21:51:38 UTC; windows
for(i in c(1,10,25,50, 100)){ print(system.time(x <- do_work(300, i, 50)))}
user system elapsed
0.09 0.02 0.24
user system elapsed
0.66 0.14 1.34
user system elapsed
1.36 0.59 2.85
user system elapsed
3.27 1.02 6.28
user system elapsed
5.94 1.98 8.70
Package: igraph
Version: 1.0.1
Title: Network Analysis and Visualization
Author: See AUTHORS file.
Maintainer: Gabor Csardi <[email protected]>
Description: Routines for simple graphs and network analysis. It can handle large graphs
very well and provides functions for generating random and regular graphs,
graph visualization, centrality methods and much more.
Depends: methods
Imports: Matrix, magrittr, NMF, irlba
Suggests: igraphdata, stats4, rgl, tcltk, graph, ape, scales
License: GPL (>= 2)
URL: http://igraph.org
SystemRequirements: gmp, libxml2
BugReports: https://github.com/igraph/igraph/issues
Collate: 'adjacency.R' 'auto.R' 'assortativity.R' 'attributes.R' 'basic.R' 'bipartite.R'
.....
NeedsCompilation: yes
Packaged: 2015-06-26 01:04:44 UTC; gaborcsardi
Repository: CRAN
Date/Publication: 2015-06-26 11:13:24
Built: R 3.3.0; i386-w64-mingw32; 2016-04-15 21:20:42 UTC; windows
for(i in c(1,10,25,50, 100)){
print(system.time(x <- do_work(300, i, 50)))
}
packageDescription('igraph')
library('igraph')
# create randomly weighted graph of n nodes
random_graph = function(n){
a <- matrix(runif(n*n, min=0, max=10), ncol=n)
diag(a) <- 0
g <- graph.adjacency(a, mode='directed', weighted = TRUE)
return(g)
}
# repeatedly get shortest paths
# graph_size: nodes in the graph
# to_count: how many destinations to route
# repeat_count: repeat the calculation for timing
do_work = function(graph_size, to_count, repeat_count){
set.seed(12345)
g = random_graph(graph_size)
toNodes = sample(1:graph_size, to_count)
for(i in 1:repeat_count){
get.shortest.paths(g, from = 1, to = toNodes, weights = NA, output = 'both', mode='in')
}
}
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