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!conda config --add channels conda-forge
!conda install -y 'icu=58.*'
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!conda install -y python-igraph networkx plotly
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import plotly
plotly.__version__
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import plotly
from plotly.graph_objs import Scatter, Layout
plotly.offline.plot({
"data": [Scatter(x=[1, 2, 3, 4], y=[4, 3, 2, 1])],
"layout": Layout(title="hello world")
})
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from igraph import *
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import urllib.request
import shutil
...
# Download the file from `url` and save it locally under `file_name`:
with urllib.request.urlopen('http://networkdata.ics.uci.edu/data/netscience/netscience.gml') as response, open('netscience.gml.txt', 'wb') as out_file:
shutil.copyfileobj(response, out_file)
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import igraph as ig
G=ig.Graph.Read_GML('netscience.gml.txt')
labels=list(G.vs['label'])
N=len(labels)
E=[e.tuple for e in G.es]# list of edges
layt=G.layout('kk') #kamada-kawai layout
type(layt)
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import plotly
help(plotly.offline.iplot)
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from plotly.offline import init_notebook_mode, iplot
init_notebook_mode()
iplot([{'x': [1, 2, 3], 'y': [5, 2, 7]}])
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import plotly.plotly as py
from plotly.graph_objs import *
Xn=[layt[k][0] for k in range(N)]
Yn=[layt[k][1] for k in range(N)]
Xe=[]
Ye=[]
for e in E:
Xe+=[layt[e[0]][0],layt[e[1]][0], None]
Ye+=[layt[e[0]][1],layt[e[1]][1], None]
trace1=Scatter(x=Xe,
y=Ye,
mode='lines',
line=Line(color='rgb(210,210,210)', width=1),
hoverinfo='none'
)
trace2=Scatter(x=Xn,
y=Yn,
mode='markers',
name='ntw',
marker=Marker(symbol='dot',
size=5,
color='#6959CD',
line=Line(color='rgb(50,50,50)', width=0.5)
),
text=labels,
hoverinfo='text'
)
axis=dict(showline=False, # hide axis line, grid, ticklabels and title
zeroline=False,
showgrid=False,
showticklabels=False,
title=''
)
width=800
height=800
layout=Layout(title= "Coauthorship network of scientists working on network theory and experiment"+\
"<br> Data source: <a href='https://networkdata.ics.uci.edu/data.php?id=11'> [1]</a>",
font= Font(size=12),
showlegend=False,
autosize=False,
width=width,
height=height,
xaxis=XAxis(axis),
yaxis=YAxis(axis),
margin=Margin(
l=40,
r=40,
b=85,
t=100,
),
hovermode='closest',
annotations=Annotations([
Annotation(
showarrow=False,
text='This igraph.Graph has the Kamada-Kawai layout',
xref='paper',
yref='paper',
x=0,
y=-0.1,
xanchor='left',
yanchor='bottom',
font=Font(
size=14
)
)
]),
)
data=Data([trace1, trace2])
fig=Figure(data=data, layout=layout)
iplot(fig, filename='Coautorship-network-igraph')
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import networkx as nx
V=range(N)# list of vertices
g=nx.Graph()
g.add_nodes_from(V)
g.add_edges_from(E)# E is the list of edges
pos=nx.fruchterman_reingold_layout(g)
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Xv=[pos[k][0] for k in range(N)]
Yv=[pos[k][1] for k in range(N)]
Xed=[]
Yed=[]
for edge in E:
Xed+=[pos[edge[0]][0],pos[edge[1]][0], None]
Yed+=[pos[edge[0]][1],pos[edge[1]][1], None]
trace3=Scatter(x=Xed,
y=Yed,
mode='lines',
line=Line(color='rgb(210,210,210)', width=1),
hoverinfo='none'
)
trace4=Scatter(x=Xv,
y=Yv,
mode='markers',
name='net',
marker=Marker(symbol='dot',
size=5,
color='#6959CD',
line=Line(color='rgb(50,50,50)', width=0.5)
),
text=labels,
hoverinfo='text'
)
annot="This networkx.Graph has the Fruchterman-Reingold layout<br>Code:"+\
"<a href='http://nbviewer.ipython.org/gist/empet/07ea33b2e4e0b84193bd'> [2]</a>"
data1=Data([trace3, trace4])
fig1=Figure(data=data1, layout=layout)
fig1['layout']['annotations'][0]['text']=annot
iplot(fig1, filename='Coautorship-network-nx')
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