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v4.0
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    v4.0

      Overlap Similarity

      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.

      In application, elements in set typically are a series of properties of an entity. For instance, when calculating the similarity between two credit applications, elements are the phone number, email, device IP, company name and so on in the application form. In general graph applications, these kinds of information are often stored as properties of a node; however, when executing this algorithm, these information is designed as nodes and incorporated into the graph.

      The range of values of overlap similarity is [0,1]; the larger the value, the more similar the two sets are.

      Basic Concept

      Set

      A set consists of multiple elements; elements in a set are unordered and distinct; the number of elements in set A is the size of set A, denoted as |A|.

      Set that consists of common elements of set A and set B is called the intersection of A and B, denoted as A⋂B.

      In the image above, set A is {b,c,e,f,g}, set B is {a,d,b,g}, intersection A⋂B is {b,g}.

      Overlap Similarity

      Known sets A and B, overlap similarity between them can be expressed as:

      Overlap similarity between sets A and B in the previous example can be calculated upon this definition: 2 / 4 = 0.5.

      Neighbors

      In the graph, Kx is the set of neighbors of node x to represent set A, Ky is the set of neighbors of node y to represent set B. Note that neither Kx nor Ky contains repeated value, nor x, nor y, so the following interferences need to be eliminated when finding neighbors by edge in the graph:

      • Multiple edges between x/y and their neighbors
      • Self-loop edges of x and y
      • Edges between x and y

      In the graph above, the red node has 3 neighbors (excludes the green node), the green node has 5 neighbors (excludes the red node), they have 2 common neighbors, their overlap similarity is 2 / 3 = 0.6667.

      Special Case

      Isolated Node, Disconnected Graph

      There is rarely computational valuable isolated node (empty set) in practice, intersection that involves isolated node is empty, and overlap similarity is 0.

      For two nodes belong to different connected components, their overlap similarity must be 0.

      Self-loop Edge

      Self-loop edge of a node does not increase the number of neighbors of the node.

      Directed Edge

      For directed edges, the algorithm ignores the direction of edges but calculates them as undirected edges.

      Command and Configuration

      • Command: algo(similarity)
      • Configurations for the parameter params():
      Name
      Type
      Default
      Specification
      Description
      ids / uuids []_id / []_uuid / Mandatory IDs or UUIDs of the first set of nodes to be calculated
      ids2 / uuids2 []_id / []_uuid / Optional IDs or UUIDs of the second set of nodes to be calculated
      type string cosine jaccard / overlap / cosine / pearson / euclideanDistance / euclidean Measurement of the similarity:
      jaccard: Jaccard Similarity
      overlap: Overlap Similarity
      cosine: Cosine Similarity
      pearson: Pearson Correlation Coefficient
      euclideanDistance: Euclidean Distance
      euclidean: Normalized Euclidean Distance
      node_schema_property []@<schema>?.<property> / Numeric node property; LTE needed; schema can be either carried or not When type is cosine / pearson / euclideanDistance / euclidean, must specify two or more node properties to form the vector; when type is jaccard / overlap, this parameter is invalid
      limit int -1 >=-1 Number of results to return; return all results if sets to -1
      top_limit int -1 >=-1 Only available in the selection mode, limit the length of selection results (top_list) of each node, return the full top_list if sets to -1

      Calculation Mode

      This algorithm has two calculation modes:

      1. Pairing mode: when two sets of valid nodes are configured, pair each node in the first set with each node in the second set (Cartesian product), similarities are calculated for all node pairs.
      2. Selection mode: when only one set (the first) of valid nodes are configured, for each node in the set, calculate its similarities with all other nodes in the graph, return the results if the similarity > 0, order the results the descending similarity.

      Examples

      Example Graph

      The example graph shows the sports liked by userA, userB, userC and userD (UUIDs are 1, 2, 3 and 4 in order):

      Task Writeback

      1. File Writeback

      Calculation Mode
      Configuration
      Data in Each Row
      Pairing mode filename node1,node2,similarity
      Selection mode filename node,top_list

      Example: Calculate overlap similarity between userC and the sets of userA, userB and userD, write the algorithm results back to file

      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
      

      Example: For each user in the set of UUID = 1,2,3,4, select the nodes that have overlap similarity above 0 with the user, write the algorithm results back to file

      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;
      

      2. Property Writeback

      Not supported by this algorithm.

      3. Statistics Writeback

      This algorithm has no statistics.

      Direct Return

      Calculation Mode
      Alias Ordinal
      Type Description Column Name
      Pairing mode 0 []perNodePair Node pair and its similarity node1, node2, similarity
      Selection mode 0 []perNode Node and its selection results node, top_list

      Example: Calculate overlap similarity between user UUID = 1 and users UUID = 2,3,4, order results in the descending similarity

      algo(similarity).params({ 
        uuids: [1], 
        uuids2: [2,3,4],
        type: "overlap"
      }) as overlap
      return overlap 
      order by overlap.similarity desc
      

      Results:

      node1 node2 similarity
      1 3 1
      1 2 0.5
      1 4 0.333333333333333

      Example: Select the node with the highest overlap similarity with nodes UUID = 1,2 respectively

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

      Results:

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

      Streaming Return

      Calculation Mode
      Alias Ordinal
      Type Description Column Name
      Pairing mode 0 []perNodePair Node pair and its similarity node1, node2, similarity
      Selection mode 0 []perNode Node and its selection results node, top_list

      Example: Calculate overlap similariy between user UUID = 3 and users UUID = 1,2,4, only return results that have similariy above 0

      algo(similarity).params({ 
        uuids: [3], 
        uuids2: [1,2,4],
        type: "overlap"
      }).stream() as overlap
      where overlap.similarity > 0
      return overlap
      

      Results:

      node1 node2 similarity
      3 1 1
      3 2 1

      Example: Select two nodes with the hightest overlap similarity with node UUID = 1

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

      Results:

      node top_list
      1 3:1.000000,2:0.500000,

      Real-time Statistics

      This algorithm has no statistics.

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