Node2Vec Walk

✓ File Writeback ✕ Property Writeback ✓ Direct Return ✓ Stream Return ✕ Stats

Overview

Diverging from the classic random walk, the Node2Vec Walk is a biased random walk which can explore neighborhoods in a BFS as well as DFS fashion. Please refer to the Node2Vec algorithm for details.

Considerations

Self-loops are also eligible to be traversed during the random walk.
If the walk starts from an isolated node without any self-loop, the walk halts after the first step as there are no adjacent edges to proceed to.
The Node2Vec Walk algorithm ignores the direction of edges but calculates them as undirected edges.

Syntax

Command：algo(random_walk_node2vec)
Parameters:

Name	Type	Spec	Default	Optional	Description
ids / uuids	[]`_id` / []`_uuid`	/	/	Yes	ID/UUID of nodes to start random walks; start from all nodes if not set
walk_length	int	≧1	`1`	Yes	Depth of each walk, i.e., the number of nodes to visit
walk_num	int	≧1	`1`	Yes	Number of walks to perform for each specified node
edge_schema_property	[]`@<schema>?.<property>`	Numeric type, must LTE	/	Yes	Edge property(-ies) to use as edge weight(s), where the values of multiple properties are summed up; nodes only walk along edges with the specified property(-ies)
p	float	>0	`1`	Yes	The return parameter; a larger value reduces the probability of returning
q	float	>0	`1`	Yes	The in-out parameter; it tends to walk at the same level when the value is greater than 1, otherwise it tends to walk far away
limit	int	≧-1	`-1`	Yes	Number of results to return, `-1` to return all results

Example

The example graph is as follows, numbers on edges are the values of edge property score:

File Writeback

Spec	Content	Description
filename	`_id`,`_id`,...	IDs of visited nodes

UQL
algo(random_walk_node2vec).params({
  walk_length: 6,
  walk_num: 2,
  p: 10000, 
  q: 0.0001
}).write({
  file:{
    filename: 'walks'
}})

Results: File walks

File
J,G,H,I,H,G,
I,H,G,F,E,C,
H,G,H,G,F,E,
G,H,G,H,I,H,
F,G,E,C,D,F,
E,F,E,F,G,H,
D,C,D,C,E,F,
C,D,A,B,A,C,
B,A,C,D,F,E,
A,B,A,B,A,C,
J,G,F,D,C,A,
I,H,G,F,E,C,
H,I,H,I,H,G,
G,F,D,C,E,F,
F,E,C,A,B,A,
E,F,E,F,D,C,
D,F,D,F,E,C,
C,D,A,B,A,C,
B,A,C,E,F,G,
A,C,A,C,E,F,

Direct Return

Alias Ordinal	Type	Description	Columns
0	[]perWalk	Array of UUIDs of visited nodes	`[_uuid, _uuid, ...]`

UQL
algo(random_walk_node2vec).params({
  ids: ['J'],
  walk_length: 6,
  walk_num: 3,
  p: 2000,
  q: 0.001
}) as walks
return walks

Results: walks

[10, 7, 6, 5, 3, 1]

[10, 7, 8, 9, 8, 7]

Stream Return

Alias Ordinal	Type	Description	Columns
0	[]perWalk	Array of UUIDs of visited nodes	`[_uuid, _uuid, ...]`

UQL
algo(random_walk_node2vec).params({
  ids: ['A'],
  walk_length: 5,
  walk_num: 10,
  p: 1000,
  q: 1,
  edge_schema_property: 'score'
}).stream() as walks
return walks

Results: walks

[1, 3, 4, 6, 5]

[1, 2, 1, 3, 5]

[1, 2, 1, 3, 4]

[1, 3, 4, 6, 7]

[1, 3, 5, 6, 7]

[1, 3, 5, 6, 4]

[1, 2, 1, 3, 5]

[1, 3, 4, 6, 7]

[1, 3, 4, 6, 5]