✓ 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; with q > 1, it tends to walk at the same level, otherwise it tends to walk far away |
| buffer_size | int | / | 1000 |
Yes | Number of results to return; a value < 0 means to return all results, otherwise to return partial 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 |
algo(random_walk_node2vec).params({
walk_length: 6,
walk_num: 2,
p: 10000,
q: 0.0001
}).write({
file:{
filename: 'walks'
}})
Results: File walks
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, ...] |
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, 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, ...] |
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, 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] |