Hi Philipp,

Not with the sample you provided.

Trying here, time decreases below that for the original graph.

(I had to change the sample to increase n and sample size, otherwise it was just too fast to capture)

In [1]: %paste

import random
import graph_tool.all as gt
n=100000
g = gt.price_network(n,10,directed=False)
%time gt.eigenvector(g)
def node_sample(g, g_vertices, n_samples):
        return random.sample(g_vertices, n_samples)
sample = node_sample(g, [x for x in g.vertices()], int(n/10))
vfilt = g.new_vertex_property('bool')
for s in sample:
        vfilt[s] = True
g_sample = gt.GraphView(g, vfilt=vfilt)
%time gt.eigenvector(g_sample)
u = gt.Graph(g_sample, prune=True)
%time gt.eigenvector(u)

## -- End pasted text --
CPU times: user 747 ms, sys: 0 ns, total: 747 ms
Wall time: 748 ms
CPU times: user 12.2 s, sys: 0 ns, total: 12.2 s
Wall time: 12.2 s
CPU times: user 335 ms, sys: 0 ns, total: 335 ms
Wall time: 335 ms

Cheers
.~´



On Wed, Jul 27, 2016 at 5:35 PM, Philipp Singer <killver@gmail.com> wrote:

Thanks for the hint, I tried that but it is the same runtime as on the filtered graph.

Best, Philipp

On 27.07.2016 17:16, Tiago de Paula Peixoto wrote:
On 27.07.2016 17:06, Philipp Singer wrote:

g = gt.price_network(10000,10,directed=False)
%time gt.eigenvector(g)

def node_sample(g, g_vertices, n_samples):
    return random.sample(g_vertices, n_samples)
sample = node_sample(g, [x for x in g.vertices()], 1000)

vfilt = g.new_vertex_property('bool')
for s in sample:
    vfilt[s] = True
g_sample = gt.GraphView(g, vfilt=vfilt)

%time gt.eigenvector(g_sample)

If you want to check if the difference is due to graph filtering, you
can compare with:

    u = gt.Graph(g_sample, prune=True)
    gt.eigenvector(u)

Best,
Tiago




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