Random Walk
Random Walk is an algorithm that provides random paths in a graph.
A random walk simulates a traversal of the graph in which the traversed relationships are chosen at random.
In a classic random walk, each relationship has the same, possibly weighted, probability of being picked.
This probability is not influenced by the previously visited nodes.
The random walk implementation of the Neo4j Graph Data Science library supports the concept of second order random walks.
This method tries to model the transition probability based on the currently visited node v
, the node t
visited before the current one, and the node x
which is the target of a candidate relationship.
Random walks are thus influenced by two parameters: the returnFactor
and the inOutFactor
:
-
The
returnFactor
is used ift
equalsx
, i.e., the random walk returns to the previously visited node. -
The
inOutFactor
is used if the distance fromt
tox
is equal to 2, i.e., the walk traverses further away from the nodet
The probabilities for traversing a relationship during a random walk can be further influenced by specifying a relationshipWeightProperty
.
A relationship property value greater than 1 will increase the likelihood of a relationship being traversed, a property value between 0 and 1 will decrease that probability.
To obtain a random walk where the transition probability is independent of the previously visited nodes both the returnFactor and the inOutFactor can be set to 1.0.
|
Running this algorithm requires sufficient memory availability. Before running this algorithm, we recommend that you read Memory Estimation. |
1. Syntax
CALL gds.beta.randomWalk.stream(
graphName: String,
configuration: Map
) YIELD
YIELD
nodeIds: List of Integer,
path: Path
Name | Type | Default | Optional | Description |
---|---|---|---|---|
graphName |
String |
|
no |
The name of a graph stored in the catalog. |
configuration |
Map |
|
yes |
Configuration for algorithm-specifics and/or graph filtering. |
Name | Type | Default | Optional | Description |
---|---|---|---|---|
List of String |
|
yes |
Filter the named graph using the given node labels. |
|
List of String |
|
yes |
Filter the named graph using the given relationship types. |
|
Integer |
|
yes |
The number of concurrent threads used for running the algorithm. |
|
String |
|
yes |
An ID that can be provided to more easily track the algorithm’s progress. |
|
sourceNodes |
List of Integer |
|
yes |
The list of nodes from which to do a random walk. |
walkLength |
Integer |
|
yes |
The number of steps in a single random walk. |
walksPerNode |
Integer |
|
yes |
The number of random walks generated for each node. |
inOutFactor |
Float |
|
yes |
Tendency of the random walk to stay close to the start node or fan out in the graph. Higher value means stay local. |
returnFactor |
Float |
|
yes |
Tendency of the random walk to return to the last visited node. A value below 1.0 means a higher tendency. |
String |
|
yes |
Name of the relationship property to use as weights to influence the probabilities of the random walks. The weights need to be >= 0. If unspecified, the algorithm runs unweighted. |
|
randomSeed |
Integer |
|
yes |
Seed value for the random number generator used to generate the random walks. |
walkBufferSize |
Integer |
|
yes |
The number of random walks to complete before starting training. |
Name | Type | Description |
---|---|---|
|
List of Integer |
The nodes of the random walk. |
|
Path |
A |
2. Examples
Consider the graph created by the following Cypher statement:
CREATE (home:Page {name: 'Home'}),
(about:Page {name: 'About'}),
(product:Page {name: 'Product'}),
(links:Page {name: 'Links'}),
(a:Page {name: 'Site A'}),
(b:Page {name: 'Site B'}),
(c:Page {name: 'Site C'}),
(d:Page {name: 'Site D'}),
(home)-[:LINKS]->(about),
(about)-[:LINKS]->(home),
(product)-[:LINKS]->(home),
(home)-[:LINKS]->(product),
(links)-[:LINKS]->(home),
(home)-[:LINKS]->(links),
(links)-[:LINKS]->(a),
(a)-[:LINKS]->(home),
(links)-[:LINKS]->(b),
(b)-[:LINKS]->(home),
(links)-[:LINKS]->(c),
(c)-[:LINKS]->(home),
(links)-[:LINKS]->(d),
(d)-[:LINKS]->(home)
CALL gds.graph.project(
'myGraph',
'*',
{ LINKS: { orientation: 'UNDIRECTED' } }
);
2.1. Without specified source nodes
myGraph
CALL gds.beta.randomWalk.stream(
'myGraph',
{
walkLength: 3,
walksPerNode: 1,
randomSeed: 42,
concurrency: 1
}
)
YIELD nodeIds, path
RETURN nodeIds, [node IN nodes(path) | node.name ] AS pages
nodeIds | pages |
---|---|
[0, 5, 3] |
[Home, Site B, Links] |
[1, 0, 6] |
[About, Home, Site C] |
[2, 0, 5] |
[Product, Home, Site B] |
[3, 6, 3] |
[Links, Site C, Links] |
[4, 3, 4] |
[Site A, Links, Site A] |
[5, 3, 5] |
[Site B, Links, Site B] |
[6, 3, 7] |
[Site C, Links, Site D] |
[7, 3, 0] |
[Site D, Links, Home] |
2.2. With specified source nodes
myGraph
with specified sourceNodesMATCH (page:Page)
WHERE page.name IN ['Home', 'About']
WITH COLLECT(page) as sourceNodes
CALL gds.beta.randomWalk.stream(
'myGraph',
{
sourceNodes: sourceNodes,
walkLength: 3,
walksPerNode: 1,
randomSeed: 42,
concurrency: 1
}
)
YIELD nodeIds, path
RETURN nodeIds, [node IN nodes(path) | node.name ] AS pages
nodeIds | pages |
---|---|
[0, 5, 3] |
[Home, Site B, Links] |
[1, 0, 6] |
[About, Home, Site C] |
Was this page helpful?