[graph-tool] Building graph from adjacency matrix
Tiago de Paula Peixoto
tiago at skewed.de
Thu Mar 20 12:52:24 CET 2014
On 03/20/2014 11:30 AM, Pietro Battiston wrote:
> I tried graph-tool after some experience with networkx, in search for
> better efficiency (attracted by ) ... but unfortunately, it seems
> like it all depends on how data is stored!
This is true. If your code essentially depends on python loops,
graph-tool will not provide any advantage.
> 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(),
> which took around 18 seconds. In graph-tool, the best alternative I
> found, based on  and , is:
> # Since the bottleneck is given by the calls to g.add_edge(),
> # numba actually slows down things a bit.
> #from numba import autojit
> def graph_from_adj_matrix(m):
> g = Graph()
> n = m.shape
> 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])
> 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 , of writing my code in C++)?
The ability of inserting many edges without calling add_edge() several
times is indeed lacking...
Because of this, I have now just added a Graph.add_edge_list() to the
git version, which takes a list of edges to be added, which can be a
numpy array. If you have a full adjacency matrix instead of an edge
list, you can do simply:
This should be much faster than the Python loop above.
Tiago de Paula Peixoto <tiago at skewed.de>
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