def viz_authorities(g: Graph, in_out, file_name: str, folder: str):
u.start_time()
auths = gm.authorities(g)
g.vs["authority_score"] = auths
my_layout = g.layout_auto()
visual_style = {
"edge_curved":
False,
"vertex_label": [
v["twitter_name"] if v["twitter_name"] != "" else
(v["twitter_id"] if (v["authority_score"] > 0) else "")
for v in g.vs
],
"target":
u.format_file(folder=folder,
file_name=file_name,
img_suffix=img_suffix),
"bbox": (1600, 1600),
"margin":
10,
"vertex_size": [x * 50 for x in auths],
"layout":
my_layout
}
# Plot the graph
drawing.plot(g, **visual_style)
u.print_delta("generate community viz graph ")
def plot_histo_degree_distribution(g: Graph,
modeInOutAll,
isLog: bool = True,
file_name: str = "",
folder: str = u.folder_img):
b = g.degree_distribution(bin_width=1)
# print('degree histogram', b)
a = []
a = g.degree(mode=modeInOutAll)
rect = plt.hist(a,
bins=range(0, (max(a))),
density=1,
stacked='True',
facecolor='b')
plt.xlabel('Number of Nodes')
plt.ylabel('Degree')
if (isLog):
plt.xscale('log')
plt.yscale('log')
plt.title('Degree Distribution')
#
print('figure is saved ')
plt.savefig(u.format_file(folder=folder, file_name=file_name))
plt.show()
def plot_scatter_frequency_degree_distribution(g: Graph,
modeInOutAll,
file_name: str,
folder: str = u.folder_img):
# from https://stackoverflow.com/questions/53958700/plotting-degree-distribution-of-networkx-digraph-using-networkx-degree-histogram
from collections import Counter
from operator import itemgetter
# G = some networkx graph
degrees = g.degree(mode=modeInOutAll)
degree_counts = Counter(degrees)
x, y = zip(*degree_counts.items())
plt.figure(1)
# prep axes
plt.xlabel('degree')
plt.xscale('log')
plt.xlim(1, max(x))
plt.ylabel('frequency')
plt.yscale('log')
plt.ylim(1, max(y))
# do plot
plt.scatter(x, y, marker='.')
plt.savefig(u.format_file(folder=folder, file_name=file_name))
plt.show()
def plot_bar_components_distribution(cl_list: VertexClustering,
isLog: bool = True,
file_name: str = "",
folder: str = u.folder_img):
cl_sizes = cl_list.sizes()
sorted_cl_sizes = sorted(cl_sizes, reverse=True)
plt.xlabel('Number of Nodes')
plt.ylabel('Cluster')
plt.title('Components Distribution')
if (isLog):
plt.xscale('log')
plt.yscale('log')
rect = plt.bar(range(len(sorted_cl_sizes)), sorted_cl_sizes, color='red')
autolabel(rect)
print('figure is saved ')
plt.savefig(u.format_file(folder=folder, file_name=file_name))
plt.show()
def viz_community(g: Graph, in_out, file_name: str, folder: str,
community_type, show_label_by_degree_percent):
u.start_time()
max_degree = max(g.degree(mode=in_out))
my_layout = g.layout_auto()
visual_style = {
"edge_curved":
False,
"vertex_label": [
# (v["twitter_id"] if ((1 - v.degree ( ) / max_degree) <= show_label_by_degree_percent) else None)
v["twitter_name"] if v["twitter_name"] != "" else
(v["twitter_id"] if
((1 - v.degree() / max_degree) <= show_label_by_degree_percent)
else None) for v in g.vs
],
"target":
u.format_file(folder=folder,
file_name=file_name,
img_suffix=img_suffix),
"bbox": (1600, 1600),
"margin":
10,
"layout":
my_layout
}
g_undirected: Graph = g.as_undirected()
communities = g_undirected.community_multilevel()
# https://stackoverflow.com/questions/43580304/python-modularity-statistic-using-louvain-package#43583445
modularity_score = g_undirected.modularity(communities.membership)
print("The modularity Q based on igraph is {}".format(modularity_score))
# Community detection
# communities = g.community_edge_betweenness (directed=False)
# communities = geant.community_fastgreedy().as_clustering()
# communities = geant.community_infomap().as_clustering()
# clusters = communities.as_clustering()
# Set edge weights based on communities
# weights = {v: len(c) for c in clusters for v in c}
# geant.es["weight"] = [weights[e.tuple[0]] + weights[e.tuple[1]] for e in geant.es]
# Plot the graph
drawing.plot(communities, mark_groups=True, **visual_style)
u.print_delta("generate community viz graph ")
def viz_diameter(g: Graph, file_name: str, folder: str):
u.start_time()
is_directed = g.is_directed()
diam = g.get_diameter(is_directed)
# Define colors used for outdegree visualization
colours = ['#CCCCCC', "#FFCC66"]
for i in range(len(g.vs)):
g.vs[i]["color"] = colours[0]
for v in g.vs.select(diam):
v["color"] = colours[1]
# g.vs.select()["color"] = [colours[1] for x in diam]
for i in range(len(g.es)):
g.es[i]["color"] = colours[0]
for eid in g.get_eids(path=diam, directed=is_directed):
g.es[eid]["color"] = colours[1]
g.vs.select(diam)["belongs_to_diameter"] = 1
my_layout = g.layout_auto()
visual_style = {
"edge_curved":
False,
"vertex_label": [(v["twitter_id"] if
(v["belongs_to_diameter"] == 1) else "")
for v in g.vs],
"target":
u.format_file(folder=folder,
file_name=file_name,
img_suffix=img_suffix),
"bbox": (1600, 1600),
"margin":
10,
"vertex_color":
g.vs["color"],
"edge_color":
g.es["color"]
# , "vertex_size": [x * 50 for x in auths], "layout": my_layout
}
# Plot the graph
drawing.plot(g, **visual_style)
u.print_delta("generate community viz graph ")
def plot_histo_components_distribution(cl_list: VertexClustering,
isLog: bool = True,
file_name: str = "",
folder: str = u.folder_img):
cl_sizes = cl_list.sizes()
sorted_cl_sizes = sorted(cl_sizes, reverse=True)
plt.xlabel('Number of Nodes')
plt.ylabel('Cluster')
plt.title('Components Distribution')
if (isLog):
plt.xscale('log')
plt.yscale('log')
rect = plt.hist(sorted_cl_sizes,
bins=range(0, (max(sorted_cl_sizes))),
density=1,
stacked='True',
facecolor='b')
print('figure is saved ')
plt.savefig(u.format_file(folder=folder, file_name=file_name))
plt.show()
def plot_plot_degree_distribution(g: Graph,
file_name='degree_distribution',
loglog=True,
marker='.',
folder: str = u.folder_img):
import pylab
u.start_time()
f = g.degree_distribution(bin_width=1)
xs, ys = zip(*[(left, count)
for left, _, count in g.degree_distribution().bins()])
# pylab.bar(xs, ys)
pylab.show()
if loglog:
pylab.xscale('log')
pylab.yscale('log')
pylab.xlabel('k')
pylab.ylabel('N')
pylab.title('Degree distribution')
pylab.plot(xs, ys, marker)
# pylab.plot(f.keys(), f.values(), marker)
pylab.savefig(u.format_file(folder=folder, file_name=file_name))
u.print_delta(' generate degree_distribution_plot')
def viz_graph(
g: Graph,
degree,
file_name: str,
folder: str,
layout: str = "auto",
show_label_by_degree: int = 0.1,
label: str = "twitter_id",
):
u.start_time()
# Define colors used for outdegree visualization
colours = ['#fecc5c', '#a31a1c']
max_degree = max(degree)
if type(max_degree) == int:
if max_degree >= 1:
num_colors = (max_degree) + 1
# https://github.com/igraph/python-igraph/issues/98
palette = RainbowPalette(n=num_colors)
color_list = [palette.get(d) for d in degree]
# Set colors according to bins
g.vs[
"color"] = color_list # [colours[x - 1] for x in digitized_degrees]
# Order vertices in bins based on outdegree
bins = np.linspace(0, max_degree, len(colours))
digitized_degrees = np.digitize(degree, bins)
# file_name = folder_img + file_name + img_suffix
my_layout = g.layout(layout=layout)
visual_style = { # Don't curve the edges
"edge_curved":
False
# Scale vertices based on degree
,
"vertex_size":
[x / max_degree * 50 + 1 for x in degree] # outdegree * 10 #
,
# https://github.com/igraph/python-igraph/issues/98
"vertex_label": [
v["twitter_name"] if v["twitter_name"] != "" else
(v[label] if
((1 - v.degree() / max_degree) <= show_label_by_degree) else "")
for v in g.vs
],
"vertex_label_size": [x / max_degree * 22 for x in degree],
"target":
u.format_file(folder=folder,
file_name=file_name,
img_suffix=img_suffix),
"bbox": (1600, 1600),
"margin":
10,
"layout":
my_layout
}
# Also color the edges
for ind, color in enumerate(g.vs["color"]):
edges = g.es.select(_source=ind)
edges["color"] = [color]
# Plot the graph
drawing.plot(g, **visual_style)
u.print_delta("generate viz graph ")