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 <tiago@skewed.de> 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 <tiago@skewed.de>


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Snehal M. Shekatkar
Pune
India