Thanks for the quick reply. It is indeed true that variance should be NaN but assortativity would be zero if I understand it correctly. Now, when instead of 'float', I use 'int' as the type for the property map, I do get 0 value for the assortativity. Thus I guess that the values are wrong and it is a bug. Am I right? From your reply, it isn't clear to me if this is a bug.
Snehal On Wed, Oct 4, 2017 at 6:37 PM, Tiago de Paula Peixoto <[email protected]> wrote: > On 04.10.2017 13:27, Snehal Shekatkar wrote: > > > > I am using gt.scalar_assortativity and I observed that it returns > non-zero > > values and big variance values even when the values on the nodes are > exactly > > same. > > > > g = gt.collection.data['karate'] > > s = g.new_vertex_property('float') > > for v in g.vertices(): > > s[v] = 0.9999 > > gt.scalar_assortativity(g, deg = s) > > > > This returns : (1.0, 8.889098493616578) > > > > I expect to see (0, 0) here. What am I missing? > > > > The scalar assortativity coefficient is undefined if the variance is zero, > since it appears in the denominator. > > The expectation that it will be zero in this case is incorrect, since the > limit where the variance goes to zero is also undefined in general. > > The proper answer in this case would be to return "NaN". I'll modify the > code in this way. > > Best, > Tiago > > -- > Tiago de Paula Peixoto <[email protected]> > > > _______________________________________________ > graph-tool mailing list > [email protected] > https://lists.skewed.de/mailman/listinfo/graph-tool > > -- Snehal M. Shekatkar Pune India
_______________________________________________ graph-tool mailing list [email protected] https://lists.skewed.de/mailman/listinfo/graph-tool
