We illustrate how an igraph Graph can be plotted with Plotly.
First we generate the Plotly plot of the network whose edges are read from a file. Then we detect the communities
in this network, and plot them with a Plotly colorscale.
In [1]:
from igraph import *
Read the edge file facebook_combined.txt, downloaded from SNAP Stanford University.
In [2]:
G=read('facebook_combined.txt', directed=False)
In [3]:
E=[e.tuple for e in G.es]# list of edges
print 'number of edges: ', len(E)
In [4]:
N=len(G.vs)# number of vertices
print 'number of vertices:', N
In [ ]:
layt=G.layout('fr') #set graph layout; 'fr'=Fruchterman-Reingold force-directed algorithm
Set data for Plotly plot:
In [ ]:
Xn=[layt[k][0] for k in range(N)]
Yn=[layt[k][1] for k in range(N)]
Xe=[]
Ye=[]
for edge in E:
Xe+=[layt[edge[0]][0],layt[edge[1]][0], None]
Ye+=[layt[edge[0]][1],layt[edge[1]][1], None]
Plotly colorscale to plot communities:
In [ ]:
custom_cs=[[0.0, '#FFFFE0'],
[0.06666666666666667, '#FFEEC1'],
[0.13333333333333333, '#FFDEA7'],
[0.2, '#FFCB91'],
[0.26666666666666666, '#FFB880'],
[0.3333333333333333, '#FFA474'],
[0.4, '#FE906A'],
[0.4666666666666667, '#F87D64'],
[0.5333333333333333, '#F06A5E'],
[0.6, '#E75758'],
[0.6666666666666666, '#DB4551'],
[0.7333333333333333, '#CF3447'],
[0.8, '#C0223B'],
[0.8666666666666667, '#B0122C'],
[0.9333333333333333, '#9E051B'],
[1.0, '#8B0000']]
In [ ]:
labels=range(N)# labels are displayed when hovering the mouse over vertices(nodes)
In [ ]:
import plotly.plotly as py
from plotly.graph_objs import *
import plotly.tools as tls
Define traces for edges and nodes:
In [ ]:
trace1=Scatter(x=Xe,
y=Ye,
mode='lines',
line=Line(color='rgb(210,210,210)', width=0.5),
hoverinfo='none'
)
trace2=Scatter(x=Xn,
y=Yn,
mode='markers',
name='Fb',
marker=Marker(symbol='dot',
size=5,
color='#C0223B',
line=Line(color='rgb(50,50,50)', width=0.5)
),
text=labels,
hoverinfo='text'
)
In [ ]:
axis=dict(showline=False, # hide axis line, grid, ticklabels and title
zeroline=False,
showgrid=False,
showticklabels=False,
title=''
)
In [ ]:
title1= "Facebook network<br> Data:"+\
"<a href='https://snap.stanford.edu/data/egonets-Facebook.html'> [1]</a>"
In [ ]:
width=700
height=700
#Plotly plot layout! Don't confuse with Graph.layout() in igraph
layout=Layout(title=title1,
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',
)
In [ ]:
data=Data([trace1, trace2])
py.sign_in('empet', 'my_api_key')
fig=Figure(data=data, layout=layout)
py.plot(fig, filename='Facebook-igraph', world_readable=False)
Detect communities in the network G:
In [5]:
community = G.community_multilevel()
In [6]:
for c in community:
print len(c)
Define a dict having as keys the vertex indices, and for each vertex_index, community_color[vertex_index]
is equal to the len of the component containing that vertex:
In [20]:
community_color={vertex_index: len(component) for component in community for vertex_index in component}
In [ ]:
trace3=Scatter(x=Xn,
y=Yn,
mode='markers',
name='Fb-comm',
marker=Marker(symbol='dot',
size=5,
color=community_color.values(),
colorscale=custom_cs,
line=Line(color='rgb(50,50,50)', width=0.5)
),
text=labels,
hoverinfo='text'
)
In [ ]:
title2= "Communities in a Facebook network, defined as an igraph Graph<br> Data:"+\
"<a href='https://snap.stanford.edu/data/egonets-Facebook.html'> [1]</a>"
In [ ]:
data1=Data([trace1, trace3])
fig=Figure(data=data1, layout=layout)
fig['layout'].update(title=title2)
py.plot(fig, filename='Facebook-Comm-igraph', world_readable=False)
In [25]:
from IPython.core.display import HTML
def css_styling():
styles = open("./custom.css", "r").read()
return HTML(styles)
css_styling()
Out[25]: