Overview

The k-Nearest Neighbors (kNN) algorithm is a classification technique that classifies the target node based on the classifications of its k nearest (most similar) nodes. kNN was proposed by T.M. Cover and P.E. Hart in 1967 and has since become one of the simplest and widely used classification algorithms:

• T.M. Cover, P.E. Hart, Nearest Neighbor Pattern Classification (1967)

Although containing the word neighbor in its name, kNN does not explicitly consider the edges between nodes when calculating similarity. Instead, it focuses solely on node properties.

Concepts

Similarity Metric

Ultipa's kNN algorithm computes pair-wise cosine similarity between the target node and all other nodes in the graph, then selects the k nodes with the highest similarity to the target node.

Vote on Classification

One node property is selected as the class label. After finding the nearest k nodes for the target node, assign the majority label among the k nodes to the target node.

If multiple labels occur with the highest frequency, the label of the node with the highest similarity will be selected.

Syntax

• Command: algo(knn)
• Parameters:

Examples

The example graph has 6 image nodes (edges are ignored), and each node has properties d1, d2, d3, d4 and type:

File Writeback

algo(knn).params({
  node_id: 1,
  node_schema_property: ['d1', 'd2', 'd3', 'd4'],
  top_k: 4,
  target_schema_property: @image.type
}).write({
  file:{
    filename: "knn"
    }
})

Results: File knn

Gold
top k : image4,0.538975
image3,0.705072
image6,0.841922
image2,0.85516

Direct Return

algo(knn).params({
  node_id: 1,
  node_schema_property: ['d1', 'd2', 'd3', 'd4'],
  top_k: 4,
  target_schema_property: @image.type
}) as a1, a2 
return a1, a2

Results: a1 and a2

Stream Return

algo(knn).params({
  node_id: 2,
  node_schema_property: ['@image.d1', '@image.d2', '@image.d3', '@image.d4'],
  top_k: 5,
  target_schema_property: @image.type
}).stream() as label
find().nodes({_uuid == 2}) as target
return case
  when target.type == label.attribute_value then 'True'
  else 'false'
end

Results: false

find().nodes({@image}) as images
call {
    with images._uuid as target
    algo(knn).params({
      node_id: target,
      node_schema_property: ['d1', 'd2', 'd3', 'd4'],
      top_k: 3,
      target_schema_property: 'type'
    }).stream() as label
    return label
}
return table(images._id, label.attribute_value)

Results: table(images._id, label.attribute_value)