Graph Visualization

CogDB can render your graph as an interactive visualization.

Creating a Graph

from cog.torque import Graph

g = Graph("people")
g.put("alice", "follows", "bob")
g.put("bob", "follows", "fred")
g.put("bob", "status", "cool_person")
g.put("charlie", "follows", "bob")
g.put("charlie", "follows", "dani")
g.put("dani", "follows", "bob")
g.put("dani", "follows", "greg")
g.put("dani", "status", "cool_person")
g.put("emily", "follows", "fred")
g.put("fred", "follows", "greg")
g.put("greg", "status", "cool_person")

Visualizing in Jupyter

Use view() and render() to visualize relationships:

g.v().tag("from").out("follows").tag("to").view("follows").render()

render() works in Jupyter notebooks. Otherwise use view(..).url to get the HTML file path.

rendered graph

Visualization Steps

tag("from") - Tag the source nodes
.out("follows") - Traverse the relationship
tag("to") - Tag the destination nodes
.view("follows") - Create a view with the edge label
.render() - Render in Jupyter

Required Tags for Rendering

When using .view() to render graph visualizations, you must use specific tag names:

Tag Name	Purpose
`from`	Identifies the source node of each edge
`to`	Identifies the target node of each edge

The graph visualization reads the from and to properties from each result to construct nodes and edges.

Important Notes:

Tag names are case-sensitive - use lowercase from and to
Both tags are required - the view needs both to draw edges between nodes
Order matters - from should mark the source vertex and to should mark the destination vertex in your traversal
Other tags are ignored - you can add additional tags (e.g., tag("source")), but only from and to are used for rendering

Export to HTML

Outside of Jupyter, get the HTML file path:

viz = g.v().tag("from").out("follows").tag("to").view("follows")
print(viz.url)  # Path to the HTML file