Graph ¶
 Title
 Graph Element
 Dependencies
 Matplotlib
 Backends
 Matplotlib
 Bokeh
import numpy as np
import pandas as pd
import holoviews as hv
import networkx as nx
hv.extension('matplotlib')
The
Graph
element provides an easy way to represent and visualize network graphs. It differs from other elements in HoloViews in that it consists of multiple subelements. The data of the
Graph
element itself are the abstract edges between the nodes. By default the element will automatically compute concrete
x
and
y
positions for the nodes and represent them using a
Nodes
element, which is stored on the Graph. The abstract edges and concrete node positions are sufficient to render the
Graph
by drawing straightline edges between the nodes. In order to supply explicit edge paths we can also declare
EdgePaths
, providing explicit coordinates for each edge to follow.
To summarize a
Graph
consists of three different components:

The
Graph
itself holds the abstract edges stored as a table of node indices. 
The
Nodes
hold the concretex
andy
positions of each node along with a nodeindex
. TheNodes
may also define any number of value dimensions, which can be revealed when hovering over the nodes or to color the nodes by. 
The
EdgePaths
can optionally be supplied to declare explicit node paths.
This reference document describes only basic functionality, for a more detailed summary on how to work with network graphs in HoloViews see the User Guide .
A simple Graph ¶
Let's start by declaring a very simple graph connecting one node to all others. If we simply supply the abstract connectivity of the
Graph
, it will automatically compute a layout for the nodes using the
layout_nodes
operation, which defaults to a circular layout:
# Declare abstract edges
N = 8
node_indices = np.arange(N)
source = np.zeros(N)
target = node_indices
padding = dict(x=(1.2, 1.2), y=(1.2, 1.2))
simple_graph = hv.Graph(((source, target),)).redim.range(**padding)
simple_graph
Accessing the nodes and edges ¶
We can easily access the
Nodes
and
EdgePaths
on the
Graph
element using the corresponding properties:
simple_graph.nodes + simple_graph.edgepaths
Additional features ¶
Next we will extend this example by supplying explicit edges, node information and edge weights. By constructing the
Nodes
explicitly we can declare an additional value dimensions, which are revealed when hovering and/or can be mapped to the color by specifying the
color_index
. We can also associate additional information with each edge by supplying a value dimension to the
Graph
itself, which we can map to a color using the
edge_color_index
.
# Node info
np.random.seed(7)
x, y = simple_graph.nodes.array([0, 1]).T
node_labels = ['Output']+['Input']*(N1)
edge_weights = np.random.rand(8)
# Compute edge paths
def bezier(start, end, control, steps=np.linspace(0, 1, 100)):
return (1steps)**2*start + 2*(1steps)*steps*control+steps**2*end
paths = []
for node_index in node_indices:
ex, ey = x[node_index], y[node_index]
paths.append(np.column_stack([bezier(x[0], ex, 0), bezier(y[0], ey, 0)]))
# Declare Graph
nodes = hv.Nodes((x, y, node_indices, node_labels), vdims='Type')
graph = hv.Graph(((source, target, edge_weights), nodes, paths), vdims='Weight')
graph.redim.range(**padding).opts(plot=dict(color_index='Type', edge_color_index='Weight'),
style=dict(cmap=['blue', 'red'], edge_cmap='viridis'))
For full documentation and the available style and plot options, use
hv.help(hv.Graph).
Download this notebook from GitHub (rightclick to download).