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v4.0
    v4.0

    Node2Vec Walk

    Overview

    Unlike the classic random walk, Node2Vec random walk adopts a second-order random walk and controls whether the walk is biased toward BFS or DFS through parameters. The sequences generated by the Node2Vec Walk algorithm are used as samples for the Node2Vec graph embedding algorithm, as detailed in the chapter Node2Vec.

    Results and Statistics

    Perform Node2Vec random walk in the graph below for 3 times, start from node 3 with a depth of 5, each edge weight is 1, set p as 10000 and q as 0.01:

    Algorithm results: 3 node arrays are contained in the returned walks

    walks
    [3, 5, 3, 1, 2]
    [3, 4, 6, 7, 8]
    [3, 1, 2, 1, 3]

    Algorithm statistics: N/A

    Command and Configuration

    • Command:algo(random_walk_node2vec)
    • Configurations for the parameter params():
    Name Type
    Default
    Specification
    Description
    ids / uuids []_id / []_uuid / / IDs or UUIDs of nodes to start the walk; all nodes to be selected if not set
    walk_length int 1 >=1 Depth of each walk, i.e. the number of nodes walking through
    walk_num int 1 >=1 Number of walks
    p float 1 >0 return parameter; the larger the value, the smaller the probability of returning
    q float 1 >0 in-out parameter that represents the probability of being to walk far away; >1 means tend to walk at the same level, >1 means tend to walk far away
    edge_schema_property []@<schema>?.<property> / Numeric edge property, LTE needed Edge weight property/properties, schema can be either carried or not; nodes only walk along edges with the specified properties and the probability of passing through these edges is proportional to the edge weight; if edge has multiple specified properties, the edge weight is the sum of these property values; the weight of all edges is 1 if not set
    limit int -1 >=-1 Number of results to return; return all results if sets to -1 or not set

    Example: Select nodes with UUID = 1,2,3 to perform Node2Vec random walk for 3 times with a depth of 5, set p as 10000 and q as 0.01

    algo(random_walk_node2vec).params({
      uuids: [1,2,3],
      walk_num: 3,
      walk_length: 5,
      p: 10000, 
      q: 100
    }) as walk
    return walk
    

    Algorithm Execution

    Task Writeback

    1. File Writeback

    Configuration
    Data in Each Row
    Description
    filename _id,_id,... IDs of nodes that walked through

    Example: Select nodes with UUID = 1,2,3 to perform Node2Vec random walk for 3 times with a depth of 5, set p as 10000 and q as 0.01, write the algorithm results back to file named path

    algo(random_walk_node2vec).params({
      uuids: [1,2,3],
      walk_num: 3,
      walk_length: 5,
      p: 10000, 
      q: 100
    }).write({
      file:{
        filename: "path"
    }})
    

    2.Property Writeback

    Not supported by this algorithm.

    3. Statistics Writeback

    This algorithm has no statistics.

    Direct Return

    Alias Ordinal Type
    Description
    Column Name
    0 []perWalk Array of UUIDs of nodes that walked through each time [_uuid, _uuid, ...]

    Example: Perform Node2Vec random walk in the whole graph for 10 times with a depth of 6, set p as 2 and q as 100, define algorithm results as alias named paths, and return the results

    algo(random_walk_node2vec).params({
      walk_num: 10,
      walk_length: 6,
      p: 2, 
      q: 100
    }) as paths return paths
    

    Streaming Return

    Alias Ordinal Type
    Description
    Column Name
    0 []perWalk Array of UUIDs of nodes that walked through each time [_uuid, _uuid, ...]

    Example: Perform Node2Vec random walk in the whole graph for 10 times with a depth of 6, set p as 2 and q as 100, and specify edge weight locates on property @follow.level, define algorithm results as alias named walk, and return the results

    algo(random_walk).params({
      walk_num: 10,
      walk_length: 6,
      p: 2, 
      q: 100
      edge_schema_property: @follow.level
    }).stream() as walk 
    return walk
    

    Real-time Statistics

    This algorithm has no statistics.

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