Network Graphs Comparison in Python/v3

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

Note: this page is part of the documentation for version 3 of Plotly.py, which is not the most recent version.
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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. Install the Python libraries with sudo pip install python-igraph and sudo pip install networkx.

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

igraph¶

In [1]:

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)

Out[1]:

igraph.layout.Layout

In [2]:

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= dict(color='rgb(210,210,210)', width=1),
               hoverinfo='none'
               )
trace2=Scatter(x=Xn,
               y=Yn,
               mode='markers',
               name='ntw',
               marker=dict(symbol='circle-dot',
                                        size=5,
                                        color='#6959CD',
                                        line=dict(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= dict(size=12),
    showlegend=False,
    autosize=False,
    width=width,
    height=height,
    xaxis=layout.XAxis(axis),
    yaxis=layout.YAxis(axis),
    margin=layout.Margin(
        l=40,
        r=40,
        b=85,
        t=100,
    ),
    hovermode='closest',
    annotations=[
           dict(
           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=dict(
            size=14
            )
            )
        ]
    )

data=[trace1, trace2]
fig=Figure(data=data, layout=layout)
py.iplot(fig, filename='Coautorship-network-igraph')

Out[2]:

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 [3]:

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)

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

In [4]:

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=dict(color='rgb(210,210,210)', width=1),
               hoverinfo='none'
               )
trace4=Scatter(x=Xv,
               y=Yv,
               mode='markers',
               name='net',
               marker=dict(symbol='circle-dot',
                             size=5,
                             color='#6959CD',
                             line=dict(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=[trace3, trace4]
fig1=Figure(data=data1, layout=layout)
fig1['layout']['annotations'][0]['text']=annot
py.iplot(fig1, filename='Coautorship-network-nx')

Out[4]:

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).

Reference¶

See https://plotly.com/python/reference/#scatter for more information and chart attribute options!