visualize()#

relationalai.std.graphs.Graph
visualize(three: bool = False, style: dict = {}, **kwargs) -> Figure

Visualize a graph.

Parameters#

NameTypeDescription
threeboolWhether or not to use the three.js 3D render engine. Defaults to False.
styledictA dictionary of visual properties for nodes and edges.
**kwargsAnyAdditional keyword arguments to pass to the gravis visualization library. See the gravis docs for full details.

Returns#

A gravis Figure object.

Example#

import relationalai as rai
from relationalai.std.graphs import Graph

# Create a model with a 'Person' type.
model = rai.Model("socialNetwork")
Person = model.Type("Person")

# Add people to the model connected by a multi-valued 'follows' property.
with model.rule():
    alice = Person.add(name="Alice")
    bob = Person.add(name="Bob")
    alice.follows.add(bob)

# Create a graph with edges from the `Person.follows` property.
graph = Graph(model)
graph.Node.extend(Person)
graph.Edge.extend(Person.follows)

# Visualize the graph.
fig = graph.visualize()

# In Jupyter notebooks, the figure is displayed inline.
# For other environments, use the `.display()` method to open the figure in a new browser window.
fig.display()

A graph with two nodes and one edge.

You may change the label, color, and size of nodes and edges:

import relationalai as rai
from relationalai.std.graphs import Graph

# Create a model with 'Person' and 'Brand' types.
model = rai.Model("socialNetwork")
Person = model.Type("Person")
Brand = model.Type("Brand")

# Add some people to the model and connect them with a multi-valued `follows` property.
with model.rule():
    alice = Person.add(name="Alice")
    bob = Person.add(name="Bob")
    acme = Brand.add(name="Acme")
    alice.follows.extend([bob, acme])
    bob.follows.add(acme)

# Create a directed graph with 'Person' and 'Brand' nodes and 'follows' edges.
graph = Graph(model)
graph.Node.extend(Person, label=Person.name, color="blue")
graph.Node.extend(Brand, label=Brand.name, color="red")
graph.Edge.extend(Person.follows, label="follows")

# Compute the PageRank of people in the graph and use it for the node's size.
with model.rule():
    person = Person()
    rank = graph.compute.pagerank(person)
    graph.Node.add(person, size=rank * 50)

fig = graph.visualize()
fig.display()

A graph with two blue nodes labeled "Bob" and "Alice" and one red node labeled "Acme." The "Bob" node is larger than the "Alice" node.

You can also describe the visual properties of nodes and edges by passing a dictionary to the style parameter. The following example produces the same visualization as the preceding example:

import relationalai as rai
from relationalai.std.graphs import Graph

model = rai.Model("socialNetwork")
Person = model.Type("Person")
Brand = model.Type("Brand")

with model.rule():
    alice = Person.add(name="Alice")
    bob = Person.add(name="Bob")
    acme = Brand.add(name="Acme")
    alice.follows.extend([bob, acme])
    bob.follows.add(acme)

graph = Graph(model)
graph.Node.extend(Person, kind="person")
graph.Node.extend(Brand, kind="brand")
graph.Edge.extend(Person.follows)


with model.rule():
    person = Person()
    rank = graph.compute.pagerank(person)
    graph.Node.add(person)

graph.visualize(style={
    "nodes": {
        # Color nodes by their 'kind' property.
        "color": lambda n: {"person": "blue", "brand": "red"}.get(n["kind"]),
        # Size nodes by their 'rank' property and scale them.
        "size": lambda n: n.get("rank", 1.0) * 50,
    },
    # Label edges as "follows."
    "edges": {"label": "follows"}
})

See Also#