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

Search
    English

      Overlap Similarity

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

      Overview

      Overlap similarity is derived from Jaccard similarity, which is also called the Szymkiewicz–Simpson coefficient. It divides the size of the intersection of two sets by the size of the smaller set with the purpose to indicate how similar the two sets are.

      Overlap similarity ranges from 0 to 1; 1 means that one set is the subset of the other or the two sets are exactly the same, 0 means that the two sets do not have any element in common.

      Concepts

      Overlap Similarity

      Given two sets A and B, the overlap similarity between them is computed as:

      In the following example, set A = {b,c,e,f,g}, set B = {a,d,b,g}, their intersection A⋂B = {b,g}, hence the overlap similarity between A and B is 2 / 4 = 0.5.

      Neighbor Set

      In Ultipa's Overlap Similarity algorithm, the following points have to be noted when collecting the neighbor sets of two target nodes to compute their similarity:

      • There is no repeated nodes in the neighbor set;
      • Self-loop is ignored;
      • Any edge between the two target nodes is ignored;
      • Edge direction is ignored.

      In the graph above, when computing the similarity between node u and node v, the neighbor sets for the two nodes are Nu = {a,b,c,d,e} and Nv = {d,e,f}, so their overlap similarity is 2 / 3 = 0.6667.

      In practice, you may need to convert some node properties into node schemas in order to calculate the similarity index that is based on common neighbors, just as the overlap Similarity. For instance, when considering the similarity between two applications, information like phone number, email, device IP, etc. of the application might have been stored as properties of @application node schema; they need to be designed as nodes and incorporated into the graph in order to be used for comparison.

      Syntax

      • Command: algo(similarity)
      • Parameters:
      Name
      Type
      Spec
      Default
      Optional
      Description
      ids / uuids []_id / []_uuid / / No ID/UUID of the first group of nodes to calculate
      ids2 / uuids2 []_id / []_uuid / / Yes ID/UUID of the second group of nodes to calculate
      type string overlap cosine No Type of similarity; for Overlap Similarity, keep it as overlap
      limit int >=-1 -1 Yes Number of results to return, -1 to return all results
      top_limit int >=-1 -1 Yes Limit the length of top_list, -1 to return the full top_list

      This algorithm has two calculation modes:

      1. Pairing: when ids/uuids and ids2/uuids2 are both configured, pairing nodes in the first group with nodes in the second group (Cartesian product) to compute pair-wise similarities.
      2. Selection: when only ids/uuids is configured, for each node in the group, computing pair-wise similarities between it and all other nodes in the graph in order to select the most similar nodes, the returned top_list includes all nodes that have similarity > 0 with it and is ordered by the descending similarity.

      Examples

      The example graph is as follows:

      File Writeback

      Calculation Mode Spec Content
      Pairing filename node1,node2,similarity
      Selection filename node,top_list
      algo(similarity).params({
        ids: "userC",
        ids2: ["userA", "userB", "userD"],
        type: "overlap"
      }).write({
        file:{ 
          filename: "sc"
        }
      })
      

      Results: File sc

      userC,userA,0.25
      userC,userB,0.5
      userC,userD,0
      
      algo(similarity).params({
        uuids: [1,2,3,4],
        type: "overlap"
      }).write({
        file:{ 
          filename: "list"
        }
      })
      

      Results: File list

      userA,userC:1.000000;userB:0.500000;userD:0.333333;
      userB,userC:1.000000;userA:0.500000;userD:0.500000;
      userC,userA:1.000000;userB:1.000000;
      userD,userB:0.500000;userA:0.333333;
      

      Direct Return

      Calculation Mode
      Alias Ordinal
      Type
      Description Columns
      Pairing 0 []perNodePair Node pair and its similarity node1, node2, similarity
      Selection 0 []perNode Node and its selection results node, top_list
      algo(similarity).params({ 
        uuids: [1], 
        uuids2: [2,3,4],
        type: "overlap"
      }) as overlap
      return overlap 
      order by overlap.similarity desc
      

      Results: overlap

      node1 node2 similarity
      1 3 1
      1 2 0.5
      1 4 0.333333333333333
      algo(similarity).params({
        uuids: [1,2],
        type: "overlap",
        top_limit: 1
      }) as top
      return top
      

      Results: top

      node top_list
      1 3:1.000000,
      2 3:1.000000,

      Stream Return

      Calculation Mode
      Alias Ordinal
      Type
      Description Columns
      Pairing 0 []perNodePair Node pair and its similarity node1, node2, similarity
      Selection 0 []perNode Node and its selection results node, top_list
      algo(similarity).params({ 
        uuids: [3], 
        uuids2: [1,2,4],
        type: "overlap"
      }).stream() as overlap
      where overlap.similarity > 0
      return overlap
      

      Results: overlap

      node1 node2 similarity
      3 1 1
      3 2 1
      algo(similarity).params({
        uuids: [1],
        type: "overlap",
        top_limit: 2
      }).stream() as top
      return top
      

      Results: top

      node top_list
      1 3:1.000000,2:0.500000,
      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