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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