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:
attachment.html (2.74 KB)
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:
attachment.html (2.74 KB)
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).
As it is written in the documentation, the second value computed is the
standard deviation of the _mean_, not the standard deviation of the
_population_, which is what you are computing. The standard deviation of
the mean is: std(w) / sqrt(len(w))
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*
This is trivial, just compute the avg_neighbour_corr with the reversed
graph:
avg_neighbour_corr(GraphView(g, reversed=True), "in", "out")
Best,
Tiago
Hi,
> 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).As it is written in the documentation, the second value computed is the
standard deviation of the _mean_, not the standard deviation of the
_population_, which is what you are computing. The standard deviation of
the mean is: std(w) / sqrt(len(w))
thanks! I missed that
> 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*This is trivial, just compute the avg_neighbour_corr with the reversed
graph:avg_neighbour_corr(GraphView(g, reversed=True), "in", "out")
sweet! I'm just starting with graph-tool and I didn't get to know the whole
api yet.
Regards,
attachment.html (2.52 KB)