Hello,
I tried graph-tool after some experience with networkx, in search for
better efficiency (attracted by [1]) ... but unfortunately, it seems
like it all depends on how data is stored!
The (several) networks I play with are dense weighted networks of 1435
nodes. They are defined in terms of their adjacency matrices, which are
pandas dataframes stored in a HDF5 store.
In order to get the relative network with networkx, I would do
dg = nx.from_numpy_matrix(hstore['my_df'].as_matrix(),
create_using=nx.DiGraph())
which took around 18 seconds. In graph-tool, the best alternative I
found, based on [2] and [3], is:
# Since the bottleneck is given by the calls to g.add_edge(),
# numba actually slows down things a bit.
#from numba import autojit
#
#@autojit
def graph_from_adj_matrix(m):
g = Graph()
n = m.shape[0]
edges = list(g.add_vertex(n=n))
c = Counter(every=10, until=n)
for i in xrange(n):
for j in xrange(n):
if m[i,j]:
g.add_edge(edges[i], edges[j])
c.count()
return g
g = graph_from_adj_matrix(hstore['my_df'].as_matrix())
... which takes around 200 seconds (and is still not even weighted!).
Is there anything trivial (or simply new) I'm missing (apart from the
possibility, mentioned in [3], of writing my code in C++)?
[1] : http://graph-tool.skewed.de/performance
[2] :
http://lists.skewed.de/pipermail/graph-tool/2011-September/000438.html
[3] : http://graph-tool.skewed.de/trac/ticket/106
Thanks,
Pietro
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