Change Password

Please enter the password.
Please enter the password. Between 8-64 characters. Not identical to your email address. Contain at least 3 of: uppercase, lowercase, numbers, and special characters.
Please enter the password.
Submit

Change Nickname

Current Nickname:
Submit

Apply New License

License Detail

Please complete this required field.

  • Ultipa Graph V4

Standalone

Please complete this required field.

Please complete this required field.

The MAC address of the server you want to deploy.

Please complete this required field.

Please complete this required field.

Cancel
Apply
ID
Product
Status
Cores
Applied Validity Period(days)
Effective Date
Excpired Date
Mac Address
Apply Comment
Review Comment
Close
Profile
  • Full Name:
  • Phone:
  • Company:
  • Company Email:
  • Country:
  • Language:
Change Password
Apply

You have no license application record.

Apply
Certificate Issued at Valid until Serial No. File
Serial No. Valid until File

Not having one? Apply now! >>>

Product Created On ID Amount (USD) Invoice
Product Created On ID Amount (USD) Invoice

No Invoice

v5.0
Search
    English
    v5.0

      Resource Allocation

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

      Overview

      The Resource Allocation algorithm operates under the assumption that nodes transmit resources to each other through their shared neighbors, who act as transmitters. In its basic form, we consider each transmitter possessing a single unit of resource, which is evenly distributed among its neighbors. Consequently, the similarity between two nodes can be gauged by the magnitude of resources that one node transmits to the other. This concept was introduced by Tao Zhou, Linyuan Lü, and Yi-Cheng Zhang in 2009:

      It is computed using the following formula:

      where N(u) is the set of nodes adjacent to u. For each common neighbor u of the two nodes, the Resource Allocation first calculates the reciprocal of its degree |N(u)|, then sums up these reciprocal values for all common neighbors.

      When calculating the degree for nodes in the graphset:

      • edges connecting two same nodes will be counted only once;
      • self-loop will be ignored.

      Higher Resource Allocation scores indicate greater similarity between nodes, while a score of 0 indicates no similarity between two nodes.

      In this example, N(D) ∩ N(E) = {B, F}, RA(D,E) = 1|N(B)| + 1|N(F)| = 14 + 13 = 0.5833.

      Considerations

      • The Resource Allocation algorithm ignores the direction of edges but calculates them as undirected edges.

      Syntax

      • Command: algo(topological_link_prediction)
      • Parameters:
      Name
      Type
      Spec
      Default
      Optional
      Description
      ids / uuids []_id / []_uuid / / No ID/UUID of the first set of nodes to calculate; each node in ids/uuids will be paired with each node in ids2/uuids2
      ids2 / uuids2 []_id / []_uuid / / No ID/UUID of the second set of nodes to calculate; each node in ids/uuids will be paired with each node in ids2/uuids2
      type string Resource_Allocation Adamic_Adar No Type of similarity; for Resource Allocation, keep it as Resource_Allocation
      limit int >=-1 -1 Yes Number of results to return, -1 to return all results

      Example

      The example graph is as follows:

      File Writeback

      Spec Content
      filename node1,node2,num
      algo(topological_link_prediction).params({
        uuids: [3],
        uuids2: [1,5,7],
        type: 'Resource_Allocation'
      }).write({
        file:{ 
          filename: 'ra'
        }
      })
      

      Results: File ra

      C,A,0.250000
      C,E,0.500000
      C,G,0.333333
      

      Direct Return

      Alias Ordinal Type
      Description
      Columns
      0 []perNodePair Node pair and its similarity node1, node2, num
      algo(topological_link_prediction).params({
        ids: 'C',
        ids2: ['A','C','E','G'],
        type: 'Resource_Allocation'
      }) as ra 
      return ra 
      

      Results: ra

      node1 node2 num
      3 1 0.25
      3 5 0.5
      3 7 0.333333333333333

      Stream Return

      Alias Ordinal Type
      Description
      Columns
      0 []perNodePair Node pair and its similarity node1, node2, num
      find().nodes() as n
      with collect(n._id) as nID
      algo(topological_link_prediction).params({
        ids: 'C',
        ids2: nID,
        type: 'Resource_Allocation'
      }).stream() as ra
      where ra.num >= 0.3
      return ra
      

      Results: ra

      node1 node2 num
      3 4 0.583333333333333
      3 5 0.5
      3 7 0.333333333333333
      Please complete the following information to download this book
      *
      公司名称不能为空
      *
      公司邮箱必须填写
      *
      你的名字必须填写
      *
      你的电话必须填写