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v4.0
    v4.0

    Cosine Similarity

    Overview

    Cosine similarity uses the cosine value of the angle formed by two N-dimensional vectors in vector space to indicate the similarity between them. Cosine similarity between two nodes in graph is calculated by using N properties of node to form two N-dimensional vectors.

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

    Basic Concept

    Vector

    Vector is one of the basic concepts in Advanced Mathematics, vectors in low dimensional spaces are relatively easy to understand and express. The following diagram shows the relationship between vectors A, B and coordinate axes in 2- and 3-dimensional spaces respectively, as well as the angle θ between them:

    When comparing two nodes in graph, N properties of node are used to form the two N-dimensional vectors.

    Cosine Similarity

    In 2-dimensional space, the formula to calculate the cosine similarity is:

    In 3-dimensional space, the formula to calculate the cosine similarity is:

    Generalize to n-dimensional space, the formula to calculate the cosine similarity is:

    Special Case

    Isolated Node, Disconnected Graph

    Theoretically, the calculation of cosine similarity between two nodes does not depend on the existence of edges in the graph. Regardless of whether the two nodes to be calculated are isolated nodes or whether they are in the same connected component, it does not affect the calculation of their cosine similarity.

    Self-loop Edge

    The calculation of cosine similarity has nothing to do with edges.

    Directed Edge

    The calculation of cosine similarity has nothing to do with 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 has product1, product2, product3 and product4 (UUIDs are 1, 2, 3 and 4 in order; edges are ignored), product node has properties price, weight, weight and height:

    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 cosine similarity between product UUID = 1 and products UUID = 2,3,4 through properties price, weight, width and height, write the algorithm results back to file

    algo(similarity).params({
      uuids: [1], 
      uuids2: [2,3,4],
      node_schema_property: [price,weight,width,height]
    }).write({
      file:{ 
        filename: "cs_result"
      }
    })
    

    Results: File cs_result

    product1,product2,0.986529
    product1,product3,0.878858
    product1,product4,0.816876
    

    Example: Calculate cosine similarity between products UUID = 1,2,3,4 and all other products in the graph respectively through properties price, weight, width and height, write the algorithm results back to file

    algo(similarity).params({
      uuids: [1,2,3,4],
      node_schema_property: [price,weight,width,height],
      type: "cosine"
    }).write({
      file:{ 
        filename: "list"
      }
    })
    

    Results: File list

    product1,product2:0.986529;product3:0.878858;product4:0.816876;
    product2,product1:0.986529;product3:0.934217;product4:0.881988;
    product3,product2:0.934217;product4:0.930153;product1:0.878858;
    product4,product3:0.930153;product2:0.881988;product1:0.816876;
    

    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 cosine similarity between product UUID = 1 and products UUID = 2,3,4 through properties price, weight, width and height, order results in the ascending similarity

    algo(similarity).params({
      uuids: [1], 
      uuids2: [2,3,4],
      node_schema_property: [price,weight,width,height],
      type: "cosine"
    }) as cs
    return cs order by cs.similarity
    

    Results:

    node1 node2 similarity
    1 4 0.816876150267203
    1 3 0.878858407519654
    1 2 0.986529413529119

    Example: Select the product with the highest cosine similarity with products UUID = 1,2 respectively through properties price, weight, width and height,

    algo(similarity).params({
      uuids: [1,2],
      type: "cosine",
      node_schema_property: [price,weight,width,height],
      top_limit: 1
    }) as top
    return top
    

    Results:

    node top_list
    1 2:0.986529,
    2 1:0.986529,

    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 cosine similarity between product UUID = 3 and products UUID = 1,2,4 through properties price, weight, width and height, only return results that have similariy above 0.9

    algo(similarity).params({
      uuids: [3], 
      uuids2: [1,2,4],
      node_schema_property: [price,weight,width,height],
      type: "cosine"
    }).stream() as cs
    where cs.similarity > 0.9 
    return cs
    

    Results:

    node1 node2 similarity
    3 2 0.934216530725663
    3 4 0.930152895706265

    Example: Select the product with the highest cosine similarity with products UUID = 1,3 respectively

    algo(similarity).params({
      uuids: [1,3],
      node_schema_property: [price,weight,width,height],
      type: "cosine",
      top_limit: 1
    }).stream() as top
    return top
    

    Results:

    node top_list
    1 2:0.986529,
    3 2:0.934217,

    Real-time Statistics

    This algorithm has no statistics.

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