k-Nearest Neighbors (Cross-Validation Version)

k-Nearest Neighbors (kNN) is often used for machine learning. You can choose the value for topK based on your experience, or using cross-validation to optimize the hyperparameters. In our library, Leave-one-out cross-validation for selecting optimal k is provided. Given a k value, we run the algorithm repeatedly using every vertex with a known label as the source vertex and predict its label. We assess the accuracy of the predictions for each value of k, and then repeat for different values of k in the given range. The goal is to find the value of k with highest predicting accuracy in the given range, for that dataset.


tg_knn_cosine_cv(SET<STRING> v_type, SET<STRING> e_type, SET<STRING> re_type,
STRING weight, STRING label, INT min_k, INT max_k) RETURNS (INT)
Characteristic Value


A list of prediction accuracy for every k value in the given range, and

the value of k with the highest predicting accuracy in the given range.

The result is available in JSON format

Input Parameters

  • SET<STRING> v_type: Vertex types to calculate distance to source vertex for

  • SET<STRING> e_type: Edge types to traverse

  • SET<STRING> re_type: Reverse edge types to traverse

  • STRING weight: Edge attribute to use as the weight of the edge

  • STRING label: Vertex attribute to recognize as the label of the vertex

  • INT min_k: lower bound of k (inclusive)

  • INT max_k: upper bound of k (inclusive)

Result Size


Time Complexity

O(max_k*E^2 / V), V = number of vertices, E = number of edges

Graph Types

Undirected or directed edges, weighted edges


Run knn_cosine_cv with min_k=2, max_k = 5. The JOSN result:

    "@@correct_rate_list": [
    "best_k": 2