Network Graph in Python

A network graph is a chart that displays relations between elements (nodes) using simple links. Network graph allows us to visualize clusters and relationships between the nodes quickly; the chart is often used in industries such as life science, cybersecurity, intelligence, etc.

Creating a network graph is straightforward. Below figure shows five nodes and the relationship between them. Node 1 has a relationship with the nodes 3, 4, and 2. The node 5 also has a relationship with nodes 2 and 4, but not with node 3.

Visualize a network graph created using networkx.

Install the Python library networkx with pip install networkx.

Create random graph

import plotly.graph_objects as go
import networkx as nx
G = nx.random_geometric_graph(200, 0.125)

Create Edges

Add edges as disconnected lines in a single trace and nodes as a scatter trace

edge_x = []
edge_y = []
for edge in G.edges():
    x0, y0 = G.nodes[edge[0]]['pos']
    x1, y1 = G.nodes[edge[1]]['pos']
    edge_x.append(x0)
    edge_x.append(x1)
    edge_x.append(None)
    edge_y.append(y0)
    edge_y.append(y1)
    edge_y.append(None)

edge_trace = go.Scatter(
    x=edge_x, y=edge_y,
    line=dict(width=0.5, color='#888'),
    hoverinfo='none',
    mode='lines')
    
node_x = []
node_y = []
for node in G.nodes():
    x, y = G.nodes[node]['pos']
    node_x.append(x)
    node_y.append(y)
    
node_trace = go.Scatter(
    x=node_x, y=node_y,
    mode='markers',
    hoverinfo='text',
    marker=dict(
        showscale=True,
        # colorscale options
        #'Greys' | 'YlGnBu' | 'Greens' | 'YlOrRd' | 'Bluered' | 'RdBu' |
        #'Reds' | 'Blues' | 'Picnic' | 'Rainbow' | 'Portland' | 'Jet' |
        #'Hot' | 'Blackbody' | 'Earth' | 'Electric' | 'Viridis' |
        colorscale='YlGnBu',
        reversescale=True,
        color=[],
        size=10,
        colorbar=dict(
            thickness=15,
            title='Node Connections',
            xanchor='left',
            titleside='right'
        ),
        line_width=2
    )
)

Color Node Points

Color node points by the number of connections.

Another option would be to size points by the number of connections i.e. node_trace.marker.size = node_adjacencies

node_adjacencies = []
node_text = []
for node, adjacencies in enumerate(G.adjacency()):
    node_adjacencies.append(len(adjacencies[1]))
    node_text.append('# of connections: '+str(len(adjacencies[1])))

node_trace.marker.color = node_adjacencies
node_trace.text = node_text

Create Network Graph

fig = go.Figure(data=[edge_trace, node_trace],
    layout=go.Layout(
        title='<br>Network graph made with Python',
        titlefont_size=16,
        showlegend=False,
        hovermode='closest',
        margin=dict(b=20,l=5,r=5,t=40),
        annotations=[ dict(
            text="Python code: <a href='https://plotly.com/ipython-
            notebooks/network-graphs/'> https://plotly.com/ipython-
            notebooks/network-graphs/</a>",
            showarrow=False,
            xref="paper", yref="paper",
            x=0.005, y=-0.002 ) ],
        xaxis=dict(showgrid=False, 
                    zeroline=False, 
                    showticklabels=False),
        yaxis=dict(showgrid=False, zeroline=False, showticklabels=False))
        )

fig.show()

Network graph made with python

Python code: https://plotly.com/ipython-notebooks/network-graphs/

Network graphs in Dash

Dash is the best way to build analytical apps in Python using Plotly figures. To run the app below, run pip install dash dash-cytoscape, click "Download" to get the code and run python app.py.

from dash import Dash, html 
import dash_cytoscape as cyto  

app = Dash(__name__)  

app.layout = html.Div([     
    html.P("Dash Cytoscape:"),     
    cyto.Cytoscape(         
        id='cytoscape',         
        elements=[             
            {'data': {'id': 'ca', 'label': 'Canada'}},              
            {'data': {'id': 'on', 'label': 'Ontario'}},              
            {'data': {'id': 'qc', 'label': 'Quebec'}},             
            {'data': {'source': 'ca', 'target': 'on'}},              
            {'data': {'source': 'ca', 'target': 'qc'}}         
        ],         
        layout={'name': 'breadthfirst'},         
        style={'width': '400px', 'height': '500px'}     
    ) 
])   

app.run_server(debug=True)

Resource: Plotly.com

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