# Graph ¶

Title
Graph Element
Dependencies
Matplotlib
Backends
Matplotlib
Bokeh
In [1]:
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 sub-elements. 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 straight-line 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 concrete  x  and  y  positions of each node along with a node  index  . The  Nodes  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:

In [2]:
# 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

Out[2]:

#### Accessing the nodes and edges ¶

We can easily access the  Nodes  and  EdgePaths  on the  Graph  element using the corresponding properties:

In [3]:
simple_graph.nodes + simple_graph.edgepaths

Out[3]:

#### 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  .

In [4]:
# Node info
np.random.seed(7)
x, y = simple_graph.nodes.array([0, 1]).T
node_labels = ['Output']+['Input']*(N-1)
edge_weights = np.random.rand(8)

# Compute edge paths
def bezier(start, end, control, steps=np.linspace(0, 1, 100)):
return (1-steps)**2*start + 2*(1-steps)*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')


For full documentation and the available style and plot options, use  hv.help(hv.Graph).