Plot various graph properties#

import nngt
import nngt.plot as nplt
from nngt.geometry import Shape

import matplotlib.pyplot as plt

plt.rcParams.update({
    'axes.edgecolor': 'grey', 'xtick.color': 'grey', 'ytick.color': 'grey',
    "figure.facecolor": (0, 0, 0, 0), "axes.facecolor": (0, 0, 0, 0),
    "axes.labelcolor": "grey", "axes.titlecolor": "grey", "text.color": "grey"
})


nngt.seed(0)

Let’s start by making a random exponential graph

shape = Shape.disk(100)

g = nngt.generation.distance_rule(5, shape=shape, nodes=1000, avg_deg=20)

Let’s plot the distances

nplt.edge_attributes_distribution(g, "distance", show=True)
Distance distribution for DR

We then compute the betweenness and see how it correlates with the distance

nbetw, ebetw = nngt.analysis.betweenness(g)

g.new_edge_attribute("betweenness", "float", values=ebetw)

nplt.correlation_to_attribute(g, "distance", "betweenness",
                              attribute_type="edge", show=True)
DR, Distance vs betweenness

Let’s check the correlations between various node properties and their degree

g.new_node_attribute("betweenness", "float", values=nbetw)

attr = ["betweenness", "clustering", "in-degree", "subgraph_centrality"]

nplt.correlation_to_attribute(g, "out-degree", attr, show=True)
DR, Out-degree vs betweenness, Out-degree vs clustering, Out-degree vs in-degree, Out-degree vs subgraph_centrality

Total running time of the script: ( 0 minutes 48.267 seconds)

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