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# Network Graphs Comparison in Python

Comparing a Network Graph created with igraph to one created with networkx in Python with Plotly.

#### New to Plotly?¶

You can set up Plotly to work in online or offline mode, or in jupyter notebooks.
We also have a quick-reference cheatsheet (new!) to help you get started!# Network Graphs with Plotly

#### Comparison¶

In this tutorial we plot the same network - the coauthorship network of scientists working on network theory and experiment - first as an igraph.Graph object, with the Kamada-Kawai layout, and then as a networkx.Graph, with the Fruchterman-Reingold layout.

The graph data are read from a gml file, posted at UC Irvine Network Data Repository:

#### igraph¶

In [4]:
import igraph as ig

labels=list(G.vs['label'])
N=len(labels)
E=[e.tuple for e in G.es]# list of edges
type(layt)

Out[4]:
igraph.layout.Layout
In [5]:
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)
py.iplot(fig, filename='Coautorship-network-igraph')

Out[5]:

#### Networkx¶

Now let us read the same gml file, define the network as a networkx.Graph, and plot it with Fruchterman Reingold layout (networkx does not provide the Kamada-Kawai layout).

Because networkx cannot read the gml file (why?!!), we define the networkx.Graph from data provided by the igraph approach above.

In [6]:
import networkx as nx

V=range(N)# list of vertices
g=nx.Graph()
g.add_edges_from(E)# E is the list of edges

pos=nx.fruchterman_reingold_layout(g)


Data for the Plotly plot of the same network but with a different layout:

In [7]:
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
py.iplot(fig1, filename='Coautorship-network-nx')

Out[7]:

Zoom in a selected region of nodes to see that edges are also plotted, but due to the node positions assigned by FR layout, they are invisible at the first sight.

We get a similar plot setting pos=nx.spring_layout(g).