Hi,
I'm trying to understand what is computed in graph_tool.correlations.
avg_neighbour_corr
but I couldn't figured out yet.
Take for example the minimal example below:
=========
from graph_tool import all as gt
import numpy as np
g = gt.Graph()
g.add_vertex(4)
g.add_edge_list([(1,0),(1,2)])
g.vp["weight"] = weights = g.new_vertex_property("double")
weights[g.vertex(0)] = 2.7
weights[g.vertex(1)] = 1.3
weights[g.vertex(2)] = 0.3
h = gt.avg_neighbour_corr(g, weights, weights)
vlist = gt.find_vertex_range(g, weights, (1,2))
w = [weights[w] for w in vlist[0].out_neighbours()]
print np.mean(w), np.std(w)
print h[0][1], h[1][1]
=========
>From the docs I'd expect h[0][1] == np.mean(w) (which is the case) and
h[1][1] == np.std(w) (which is not the case).
I'd appreciate any clarification/reference on this subject. In fact, I got
to this issue trying to implement the analogous function to
graph_tool.correlations.avg_neighbour_corr but looking at
*in_neighbours* instead
of *out_neighbours* and when I tried to replicate the native function I
noticed that the average was almost the same (I think I didn't get yet how
is exactly handle the case of having many vertices in the same bin) but
the standard deviation was quite different.
Regards,
--
Santiago Videla
http://www.linkedin.com/in/svidela
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