I am very sorry to bug you so long, but I am bewildered now.

Of course, the degree is a discrete variable. I said we treat it as a
continuous variable because we don't categorize the degree values like we
do for a gender. For example, we don't treat degree values 25 and 26 as two
different categories. (Formula 7.82 in Newman's book).

I am discarding repetitions because I wanted to treat unique degree values
as discrete types. For example, to study mixing by genders, I will have
first to find out the unique gender values. What is wrong with this?

Thank you

On Fri, Oct 6, 2017 at 4:21 PM, Tiago de Paula Peixoto <[email protected]>
wrote:

> On 06.10.2017 07:37, Snehal Shekatkar wrote:
> > First, the formula for gt.assortativity implies that we are talking about
> > discrete categories for the vertices. If this is true, how can we use it
> at
> > all for "degree" since we treat that as a continuous variable? Thus,  I
> > don't understand what does "in", "out" and "total" do in this formula.
>
> Degrees are discrete, not continuous.
>
> > Second, I tried implementing the formula itself assuming that the actual
> > degree values to be discrete types and my code gives different results
> than
> > the result given by gt.assortativity. I agree that I might be
> interpreting
> > the whole thing in a different fashion and I would be very happy to
> > understand it. My code:
> >
> > import numpy as np
> > import graph_tool.all as gt
> >
> > # Load a graph
> > g = gt.collection.data['karate']
> >
> > # Unique degree values or types
> > deg_vals = list(set([v.out_degree() for v in g.vertices()]))
> > n = len(deg_vals)
>
> Why are you doing this? The moment you discard repetitions, all the
> fractions you compute will be wrong.
>
> > Why are these two values different?
>
> Because they come from different algorithms.
>
> Best,
> Tiago
>
> --
> Tiago de Paula Peixoto <[email protected]>
>
>
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>
>


-- 
Snehal M. Shekatkar
Pune
India
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