Make an Interactive Network Visualization¶

This notebook includes code for creating interactive network visualizations with the Python libraries NetworkX and Bokeh. The notebook begins with code for a basic network visualization then progressively demonstrates how to add more information and functionality, such as:

sizing and coloring nodes by degree
sizing and coloring nodes by modularity class
adding responsive highlighting when hovering over nodes and edges
adding node labels

Dataset¶

Game of Thrones¶

Lucky it might be, and red it certainly was, but Ygritte’s hair was such a tangle that Jon was tempted to ask her if she only brushed it at the changing of the seasons.

—George R. R. Martin, A Song of Ice and Fire

Import Libraries¶

import pandas as pd
import networkx
import matplotlib.pyplot as plt
import numpy as np

Install and Import Bokeh¶

#!pip install bokeh

from bokeh.io import output_notebook, show, save

To view interactive Bokeh visualizations in a Jupyter notebook, you need to run this cell:

output_notebook()

Loading BokehJS ...

Create Network From Pandas DataFrame¶

We read in a CSV file of Game of Thrones network data from Andrew Beveridge and Jie Shan’s paper, “Network of Thrones.”

got_df = pd.read_csv('../data/got-edges.csv')

got_df

	Source	Target	Weight
0	Aemon	Grenn	5
1	Aemon	Samwell	31
2	Aerys	Jaime	18
3	Aerys	Robert	6
4	Aerys	Tyrion	5
...	...	...	...
347	Walder	Petyr	6
348	Walder	Roslin	6
349	Walton	Jaime	10
350	Ygritte	Qhorin	7
351	Ygritte	Rattleshirt	9

352 rows × 3 columns

Then we make a network with networkx.from_pandas_edgelist():

G = networkx.from_pandas_edgelist(got_df, 'Source', 'Target', 'Weight')

Basic Network¶

The code below shows how to make a basic network viz that includes Hover Tooltips (a text box that will display when a user hovers over nodes) as well as Zoom and Pan/Drag functionality.

For more details about visualizing network graphs with Bokeh, see the documentation.

from bokeh.io import output_notebook, show, save
from bokeh.models import Range1d, Circle, ColumnDataSource, MultiLine
from bokeh.plotting import figure
from bokeh.plotting import from_networkx

#Choose a title!
title = 'Game of Thrones Network'

#Establish which categories will appear when hovering over each node
HOVER_TOOLTIPS = [("Character", "@index")]

#Create a plot — set dimensions, toolbar, and title
plot = figure(tooltips = HOVER_TOOLTIPS,
              tools="pan,wheel_zoom,save,reset", active_scroll='wheel_zoom',
            x_range=Range1d(-10.1, 10.1), y_range=Range1d(-10.1, 10.1), title=title)

#Create a network graph object with spring layout
# https://networkx.github.io/documentation/networkx-1.9/reference/generated/networkx.drawing.layout.spring_layout.html
network_graph = from_networkx(G, networkx.spring_layout, scale=10, center=(0, 0))

#Set node size and color
network_graph.node_renderer.glyph = Circle(size=15, fill_color='skyblue')

#Set edge opacity and width
network_graph.edge_renderer.glyph = MultiLine(line_alpha=0.5, line_width=1)

#Add network graph to the plot
plot.renderers.append(network_graph)

show(plot)
#save(plot, filename=f"{title}.html")

Network with Nodes Sized and Colored By Attribute (Degree)¶

The code below shows how to size and color nodes by degree.

Include Bokeh color palettes

from bokeh.io import output_notebook, show, save
from bokeh.models import Range1d, Circle, ColumnDataSource, MultiLine
from bokeh.plotting import figure
from bokeh.plotting import from_networkx
from bokeh.palettes import Blues8, Reds8, Purples8, Oranges8, Viridis8, Spectral8
from bokeh.transform import linear_cmap

Calculate degree for each node and add as node attribute

degrees = dict(networkx.degree(G))
networkx.set_node_attributes(G, name='degree', values=degrees)

Slightly adjust degree so that the nodes with very small degrees are still visible

number_to_adjust_by = 5
adjusted_node_size = dict([(node, degree+number_to_adjust_by) for node, degree in networkx.degree(G)])
networkx.set_node_attributes(G, name='adjusted_node_size', values=adjusted_node_size)

#Choose attributes from G network to size and color by — setting manual size (e.g. 10) or color (e.g. 'skyblue') also allowed
size_by_this_attribute = 'adjusted_node_size'
color_by_this_attribute = 'adjusted_node_size'

#Pick a color palette — Blues8, Reds8, Purples8, Oranges8, Viridis8
color_palette = Blues8

#Choose a title!
title = 'Game of Thrones Network'

#Establish which categories will appear when hovering over each node
HOVER_TOOLTIPS = [
       ("Character", "@index"),
        ("Degree", "@degree")
]

#Create a plot — set dimensions, toolbar, and title
plot = figure(tooltips = HOVER_TOOLTIPS,
              tools="pan,wheel_zoom,save,reset", active_scroll='wheel_zoom',
            x_range=Range1d(-10.1, 10.1), y_range=Range1d(-10.1, 10.1), title=title)

#Create a network graph object
# https://networkx.github.io/documentation/networkx-1.9/reference/generated/networkx.drawing.layout.spring_layout.html\
network_graph = from_networkx(G, networkx.spring_layout, scale=10, center=(0, 0))

#Set node sizes and colors according to node degree (color as spectrum of color palette)
minimum_value_color = min(network_graph.node_renderer.data_source.data[color_by_this_attribute])
maximum_value_color = max(network_graph.node_renderer.data_source.data[color_by_this_attribute])
network_graph.node_renderer.glyph = Circle(size=size_by_this_attribute, fill_color=linear_cmap(color_by_this_attribute, color_palette, minimum_value_color, maximum_value_color))

#Set edge opacity and width
network_graph.edge_renderer.glyph = MultiLine(line_alpha=0.5, line_width=1)

plot.renderers.append(network_graph)

show(plot)
#save(plot, filename=f"{title}.html")

Network with Nodes Colored By Attribute (Community)¶

The code below shows how to size and color nodes by modularity class.

Include community module

from bokeh.io import output_notebook, show, save
from bokeh.models import Range1d, Circle, ColumnDataSource, MultiLine
from bokeh.plotting import figure
from bokeh.plotting import from_networkx
from bokeh.palettes import Blues8, Reds8, Purples8, Oranges8, Viridis8, Spectral8
from bokeh.transform import linear_cmap
from networkx.algorithms import community

Calculate degree for each node and add as node attribute

degrees = dict(networkx.degree(G))
networkx.set_node_attributes(G, name='degree', values=degrees)

Slightly adjust degree so that the nodes with very small degrees are still visible

number_to_adjust_by = 5
adjusted_node_size = dict([(node, degree+number_to_adjust_by) for node, degree in networkx.degree(G)])
networkx.set_node_attributes(G, name='adjusted_node_size', values=adjusted_node_size)

Calculate communities

communities = community.greedy_modularity_communities(G)

Add modularity class and color as attributes to network graph

# Create empty dictionaries
modularity_class = {}
modularity_color = {}
#Loop through each community in the network
for community_number, community in enumerate(communities):
    #For each member of the community, add their community number and a distinct color
    for name in community: 
        modularity_class[name] = community_number
        modularity_color[name] = Spectral8[community_number]

# Add modularity class and color as attributes from the network above
networkx.set_node_attributes(G, modularity_class, 'modularity_class')
networkx.set_node_attributes(G, modularity_color, 'modularity_color')

#Choose attributes from G network to size and color by — setting manual size (e.g. 10) or color (e.g. 'skyblue') also allowed
size_by_this_attribute = 'adjusted_node_size'
color_by_this_attribute = 'modularity_color'
#Pick a color palette — Blues8, Reds8, Purples8, Oranges8, Viridis8
color_palette = Blues8
#Choose a title!
title = 'Game of Thrones Network'

#Establish which categories will appear when hovering over each node
HOVER_TOOLTIPS = [
       ("Character", "@index"),
        ("Degree", "@degree"),
         ("Modularity Class", "@modularity_class"),
        ("Modularity Color", "$color[swatch]:modularity_color"),
]

#Create a plot — set dimensions, toolbar, and title
plot = figure(tooltips = HOVER_TOOLTIPS,
              tools="pan,wheel_zoom,save,reset, tap", active_scroll='wheel_zoom',
            x_range=Range1d(-10.1, 10.1), y_range=Range1d(-10.1, 10.1), title=title)

#Create a network graph object
# https://networkx.github.io/documentation/networkx-1.9/reference/generated/networkx.drawing.layout.spring_layout.html
network_graph = from_networkx(G, networkx.spring_layout, scale=10, center=(0, 0))

#Set node sizes and colors according to node degree (color as category from attribute)
network_graph.node_renderer.glyph = Circle(size=size_by_this_attribute, fill_color=color_by_this_attribute)

#Set edge opacity and width
network_graph.edge_renderer.glyph = MultiLine(line_alpha=0.5, line_width=1)

plot.renderers.append(network_graph)

show(plot)
#save(plot, filename=f"{title}.html")

Network with Responsive Highlighting¶

The code below shows how to create responsive highlighting when a user hovers over nodes or edges, which you can read more about in Bokeh’s NetworkX Integration documentation.

Include EdgesAndLinkedNodes, NodesAndLinkedEdges

from bokeh.io import output_notebook, show, save
from bokeh.models import Range1d, Circle, ColumnDataSource, MultiLine, EdgesAndLinkedNodes, NodesAndLinkedEdges
from bokeh.plotting import figure
from bokeh.plotting import from_networkx
from bokeh.palettes import Blues8, Reds8, Purples8, Oranges8, Viridis8, Spectral8
from bokeh.transform import linear_cmap
from networkx.algorithms import community

Calculate degree for each node and add as node attribute

degrees = dict(networkx.degree(G))
networkx.set_node_attributes(G, name='degree', values=degrees)

Slightly adjust degree so that the nodes with very small degrees are still visible

number_to_adjust_by = 5
adjusted_node_size = dict([(node, degree+number_to_adjust_by) for node, degree in networkx.degree(G)])
networkx.set_node_attributes(G, name='adjusted_node_size', values=adjusted_node_size)

Calculate communities

communities = community.greedy_modularity_communities(G)

Add modularity class and color as attributes to network graph

# Create empty dictionaries
modularity_class = {}
modularity_color = {}
#Loop through each community in the network
for community_number, community in enumerate(communities):
    #For each member of the community, add their community number and a distinct color
    for name in community: 
        modularity_class[name] = community_number
        modularity_color[name] = Spectral8[community_number]

# Add modularity class and color as attributes from the network above
networkx.set_node_attributes(G, modularity_class, 'modularity_class')
networkx.set_node_attributes(G, modularity_color, 'modularity_color')

from bokeh.models import EdgesAndLinkedNodes, NodesAndLinkedEdges

#Choose colors for node and edge highlighting
node_highlight_color = 'white'
edge_highlight_color = 'black'

#Choose attributes from G network to size and color by — setting manual size (e.g. 10) or color (e.g. 'skyblue') also allowed
size_by_this_attribute = 'adjusted_node_size'
color_by_this_attribute = 'modularity_color'

#Pick a color palette — Blues8, Reds8, Purples8, Oranges8, Viridis8
color_palette = Blues8

#Choose a title!
title = 'Game of Thrones Network'

#Establish which categories will appear when hovering over each node
HOVER_TOOLTIPS = [
       ("Character", "@index"),
        ("Degree", "@degree"),
         ("Modularity Class", "@modularity_class"),
        ("Modularity Color", "$color[swatch]:modularity_color"),
]

#Create a plot — set dimensions, toolbar, and title
plot = figure(tooltips = HOVER_TOOLTIPS,
              tools="pan,wheel_zoom,save,reset", active_scroll='wheel_zoom',
            x_range=Range1d(-10.1, 10.1), y_range=Range1d(-10.1, 10.1), title=title)

#Create a network graph object
# https://networkx.github.io/documentation/networkx-1.9/reference/generated/networkx.drawing.layout.spring_layout.html
network_graph = from_networkx(G, networkx.spring_layout, scale=10, center=(0, 0))

#Set node sizes and colors according to node degree (color as category from attribute)
network_graph.node_renderer.glyph = Circle(size=size_by_this_attribute, fill_color=color_by_this_attribute)
#Set node highlight colors
network_graph.node_renderer.hover_glyph = Circle(size=size_by_this_attribute, fill_color=node_highlight_color, line_width=2)
network_graph.node_renderer.selection_glyph = Circle(size=size_by_this_attribute, fill_color=node_highlight_color, line_width=2)

#Set edge opacity and width
network_graph.edge_renderer.glyph = MultiLine(line_alpha=0.5, line_width=1)
#Set edge highlight colors
network_graph.edge_renderer.selection_glyph = MultiLine(line_color=edge_highlight_color, line_width=2)
network_graph.edge_renderer.hover_glyph = MultiLine(line_color=edge_highlight_color, line_width=2)

    #Highlight nodes and edges
network_graph.selection_policy = NodesAndLinkedEdges()
network_graph.inspection_policy = NodesAndLinkedEdges()

plot.renderers.append(network_graph)

show(plot)
#save(plot, filename=f"{title}.html")

Network with Labels¶

The code below shows how to create node labels, which you can read more about in Bokeh’s label documentation.

Include LabelSet

from bokeh.io import output_notebook, show, save
from bokeh.models import Range1d, Circle, ColumnDataSource, MultiLine, EdgesAndLinkedNodes, NodesAndLinkedEdges, LabelSet
from bokeh.plotting import figure
from bokeh.plotting import from_networkx
from bokeh.palettes import Blues8, Reds8, Purples8, Oranges8, Viridis8, Spectral8
from bokeh.transform import linear_cmap
from networkx.algorithms import community

Calculate degree for each node and add as node attribute

degrees = dict(networkx.degree(G))
networkx.set_node_attributes(G, name='degree', values=degrees)

Slightly adjust degree so that the nodes with very small degrees are still visible

number_to_adjust_by = 5
adjusted_node_size = dict([(node, degree+number_to_adjust_by) for node, degree in networkx.degree(G)])
networkx.set_node_attributes(G, name='adjusted_node_size', values=adjusted_node_size)

Calculate communities

communities = community.greedy_modularity_communities(G)

Add modularity class and color as attributes to network graph

# Create empty dictionaries
modularity_class = {}
modularity_color = {}
#Loop through each community in the network
for community_number, community in enumerate(communities):
    #For each member of the community, add their community number and a distinct color
    for name in community: 
        modularity_class[name] = community_number
        modularity_color[name] = Spectral8[community_number]

#Choose colors for node and edge highlighting
node_highlight_color = 'white'
edge_highlight_color = 'black'

#Choose attributes from G network to size and color by — setting manual size (e.g. 10) or color (e.g. 'skyblue') also allowed
size_by_this_attribute = 'adjusted_node_size'
color_by_this_attribute = 'modularity_color'

#Pick a color palette — Blues8, Reds8, Purples8, Oranges8, Viridis8
color_palette = Blues8

#Choose a title!
title = 'Game of Thrones Network'

#Establish which categories will appear when hovering over each node
HOVER_TOOLTIPS = [
       ("Character", "@index"),
        ("Degree", "@degree"),
         ("Modularity Class", "@modularity_class"),
        ("Modularity Color", "$color[swatch]:modularity_color"),
]

#Create a plot — set dimensions, toolbar, and title
plot = figure(tooltips = HOVER_TOOLTIPS,
              tools="pan,wheel_zoom,save,reset", active_scroll='wheel_zoom',
            x_range=Range1d(-10.1, 10.1), y_range=Range1d(-10.1, 10.1), title=title)

#Create a network graph object
# https://networkx.github.io/documentation/networkx-1.9/reference/generated/networkx.drawing.layout.spring_layout.html
network_graph = from_networkx(G, networkx.spring_layout, scale=10, center=(0, 0))

#Set node sizes and colors according to node degree (color as category from attribute)
network_graph.node_renderer.glyph = Circle(size=size_by_this_attribute, fill_color=color_by_this_attribute)
#Set node highlight colors
network_graph.node_renderer.hover_glyph = Circle(size=size_by_this_attribute, fill_color=node_highlight_color, line_width=2)
network_graph.node_renderer.selection_glyph = Circle(size=size_by_this_attribute, fill_color=node_highlight_color, line_width=2)

#Set edge opacity and width
network_graph.edge_renderer.glyph = MultiLine(line_alpha=0.3, line_width=1)
#Set edge highlight colors
network_graph.edge_renderer.selection_glyph = MultiLine(line_color=edge_highlight_color, line_width=2)
network_graph.edge_renderer.hover_glyph = MultiLine(line_color=edge_highlight_color, line_width=2)

    #Highlight nodes and edges
network_graph.selection_policy = NodesAndLinkedEdges()
network_graph.inspection_policy = NodesAndLinkedEdges()

plot.renderers.append(network_graph)

#Add Labels
x, y = zip(*network_graph.layout_provider.graph_layout.values())
node_labels = list(G.nodes())
source = ColumnDataSource({'x': x, 'y': y, 'name': [node_labels[i] for i in range(len(x))]})
labels = LabelSet(x='x', y='y', text='name', source=source, background_fill_color='white', text_font_size='10px', background_fill_alpha=.7)
plot.renderers.append(labels)

show(plot)
#save(plot, filename=f"{title}.html")

Introduction to Cultural Analytics & Python

Make an Interactive Network Visualization¶

Dataset¶

Game of Thrones¶

Import Libraries¶

Install and Import Bokeh¶

Create Network From Pandas DataFrame¶

Basic Network¶

Network with Nodes Sized and Colored By Attribute (Degree)¶

Network with Nodes Colored By Attribute (Community)¶

Network with Responsive Highlighting¶

Network with Labels¶