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v4.0
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    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
      buffer_size int 1000 / Number of results to return; < 0 means to return all results, ≧ 0 means to return partial results

      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, return the results that walked more than 5 steps

      algo(random_walk_node2vec).params({
        walk_num: 10,
        walk_length: 6,
        p: 2, 
        q: 100,
        edge_schema_property: @follow.level
      }).stream() as walk 
      where size(walk) > 5
      return walk
      

      Real-time Statistics

      This algorithm has no statistics.

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