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v5.0
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    English
    v5.0

      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
      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]
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