24.8 Layouts con python
El módulo base tiene incorporadas varias configuraciones de disposición de los nodos en el lienzo (layouts). Por defecto organiza los nodos según el algoritmo de Fruchterman Reingold (fruchterman_reingold_layout). Pero también: circular, random, spring (Fruchterman-Reingold force-directed), planar (sin intercepción entre enlaces), shell (círculos concentricos), spiral (como su nombre indica), multipartite (varias capas de lineas rectas) o spectral (explicado en el literal anterior), kamada_kawai, arf (fuerzas atractivas-repulsivas).
import networkx as nx
import matplotlib.pyplot as plt
from adjustText import adjust_text
from faker import Faker
import random
# ----------------------------
# 1) Datos sintéticos
# ----------------------------
fraude = Faker()
fraude.random.seed(4321)
random.seed(1516)
num_nodos = 100
nombre1, nombre2 = set(), set()
for _ in range(num_nodos):
nombre1.add(fraude.first_name())
nombre2.add(fraude.first_name())
dnombres = list(zip(nombre1, nombre2))
G = nx.Graph()
G.add_edges_from(dnombres)
G.remove_edges_from(nx.selfloop_edges(G))
# Asigna país a cada nodo y color
todos = nombre1.union(nombre2)
paises = ["Colombia", "Venezuela"]
nombres_pais = {name: fraude.random.choice(paises) for name in todos}
colores = {'Colombia': 'steelblue', 'Venezuela': 'firebrick'}
color_nodos = [colores[nombres_pais[n]] for n in G.nodes()]
# ----------------------------
# 2) Definir layouts a comparar
# ----------------------------
layouts = {
"Spring (Fruchterman–Reingold)": lambda g: nx.spring_layout(g,
seed=42, k=None),
"Kamada–Kawai": lambda g: nx.kamada_kawai_layout(g),
"arf": lambda g: nx.arf_layout(g),
"Circular": lambda g: nx.circular_layout(g)
}
# ----------------------------
# 3) Dibujar en grilla 2×2
# ----------------------------
fig, axes = plt.subplots(2, 2, figsize=(12, 9), constrained_layout=True)
axes = axes.ravel()
for ax, (titulo, layout_fn) in zip(axes, layouts.items()):
pos = layout_fn(G)
# aristas y nodos
nx.draw_networkx_edges(G, pos, ax=ax, width=0.5, alpha=0.6)
nx.draw_networkx_nodes(G, pos, ax=ax,
node_color=color_nodos, node_size=50, alpha=0.85)
# etiquetas con adjust_text
texts = [ax.text(pos[n][0], pos[n][1], n, fontsize=6) for n in G.nodes()]
adjust_text(texts, ax=ax, only_move={'points':'xy', 'texts':'xy'},
autoalign='y', expand_points=(1.2, 1.2), expand_text=(1.2, 1.2))
ax.set_title(titulo)
ax.axis('off')## <matplotlib.collections.LineCollection object at 0x0000012D86852E80>
## <matplotlib.collections.PathCollection object at 0x0000012D86852610>
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## Text(0.5, 1.0, 'Spring (Fruchterman–Reingold)')
## (-1.1879741362010767, 1.1318073725728262, -1.2048545526526444, 1.1558502922016383)
## <matplotlib.collections.LineCollection object at 0x0000012D8704E970>
## <matplotlib.collections.PathCollection object at 0x0000012D85EE0AF0>
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-0.598554479893403, 'Jeremy'), Text(0.7982891293862631, -0.5363432923934628, 'Carl'), Text(0.9071977876872221, -0.5184609130899807, 'Andrew'), Text(1.0381233521638205, -0.4903231343211003, 'Keith'), Text(0.9330402423876079, -0.43563585270828475, 'Kelly'), Text(1.0186766303882386, -0.4329408840624053, 'Gloria'), Text(1.0676500143755812, -0.37156118926616966, 'Shannon'), Text(1.0783858816625056, -0.29789755741501744, 'Jeffrey'), Text(0.977002183317139, -0.23519937807465097, 'Angel'), Text(0.9988411261803485, -0.17782602105578293, 'Derrick'), Text(1.1229853512620434, -0.20092998651826077, 'Leah'), Text(1.097151696892535, -0.1428385574429616, 'Todd'), Text(1.0404423096821303, -0.059508353595550734, 'Andrea')], [])
## Text(0.5, 1.0, 'Circular')
## (-1.2099999989555286, 1.2099999999007516, -1.2096674439279969, 1.209667449347314)## Text(0.5, 0.98, 'Grafo con cuatro layouts (color por país)')