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@rbnvrw
Last active May 7, 2022 09:34
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python-igraph example
from igraph import *
import numpy as np
# Create the graph
vertices = [i for i in range(7)]
edges = [(0,2),(0,1),(0,3),(1,0),(1,2),(1,3),(2,0),(2,1),(2,3),(3,0),(3,1),(3,2),(2,4),(4,5),(4,6),(5,4),(5,6),(6,4),(6,5)]
g = Graph(vertex_attrs={"label":vertices}, edges=edges, directed=True)
visual_style = {}
# Scale vertices based on degree
outdegree = g.outdegree()
visual_style["vertex_size"] = [x/max(outdegree)*25+50 for x in outdegree]
# Set bbox and margin
visual_style["bbox"] = (800,800)
visual_style["margin"] = 100
# Define colors used for outdegree visualization
colours = ['#fecc5c', '#a31a1c']
# Order vertices in bins based on outdegree
bins = np.linspace(0, max(outdegree), len(colours))
digitized_degrees = np.digitize(outdegree, bins)
# Set colors according to bins
g.vs["color"] = [colours[x-1] for x in digitized_degrees]
# Also color the edges
for ind, color in enumerate(g.vs["color"]):
edges = g.es.select(_source=ind)
edges["color"] = [color]
# Don't curve the edges
visual_style["edge_curved"] = False
# Community detection
communities = g.community_edge_betweenness(directed=True)
clusters = communities.as_clustering()
# Set edge weights based on communities
weights = {v: len(c) for c in clusters for v in c}
g.es["weight"] = [weights[e.tuple[0]] + weights[e.tuple[1]] for e in g.es]
# Choose the layout
N = len(vertices)
visual_style["layout"] = g.layout_fruchterman_reingold(weights=g.es["weight"], maxiter=1000, area=N**3, repulserad=N**3)
# Plot the graph
plot(g, **visual_style)
from igraph import *
vertices = ["one", "two", "three"]
edges = [(0,2),(2,1),(0,1)]
g = Graph(vertex_attrs={"label": vertices}, edges=edges, directed=True)
plot(g)
from igraph import *
import numpy as np
# Create the graph
vertices = ["one", "two", "three"]
edges = [(0,2),(2,1),(0,1)]
g = Graph(vertex_attrs={"label": vertices}, edges=edges, directed=True)
visual_style = {}
# Scale vertices based on degree
outdegree = g.outdegree()
visual_style["vertex_size"] = [x/max(outdegree)*50+110 for x in outdegree]
# Set bbox and margin
visual_style["bbox"] = (800,800)
visual_style["margin"] = 100
# Define colors used for outdegree visualization
colours = ['#fecc5c', '#a31a1c']
# Order vertices in bins based on outdegree
bins = np.linspace(0, max(outdegree), len(colours))
digitized_degrees = np.digitize(outdegree, bins)
# Set colors according to bins
g.vs["color"] = [colours[x-1] for x in digitized_degrees]
# Also color the edges
for ind, color in enumerate(g.vs["color"]):
edges = g.es.select(_source=ind)
edges["color"] = [color]
# Don't curve the edges
visual_style["edge_curved"] = False
# Plot the graph
plot(g, **visual_style)
@farhankhwaja
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I am getting a "plot not available error". I tried installing cairo/pycairo but I am getting a conflict i.e. python3.5 pycairo are conflicting. Is there a way to plot the graph anyother way? Or how can I res0lve the issue.

@abitofalchemy
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What is the 'plot'ting framework you are using? I get the following if I add %matplotlib inline import matplotlib.pyplot as plt then I try to plot as plt.plot( ... )

@rasangaks
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I am getting a "plot not available error". I tried installing cairo/pycairo but I am getting a conflict i.e. python3.5 pycairo are conflicting. Is there a way to plot the graph anyother way? Or how can I res0lve the issue.

try installing this sudo pip install cairocffi

@MohammadHeydari
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I already installed pycairo by pip install cairocffi but the error is still there which is : "plot not available"

@MohammadHeydari
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I already installed pycairo by pip install cairocffi but the error is still there which is : "plot not available"

issue eliminated. the solution is import cairo.

@apolinario-souza
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just write import cairocffi as cairo

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