k-Nearest Neighbors (Batch Version)
This algorithm is a batch version of the k-Nearest Neighbors, Cosine Neighbor Similarity, single vertex. It makes a prediction for every vertex whose label is not known (i.e., the attribute for the known label is empty), based on its k nearest neighbors' labels.
Specifications
tg_knn_cosine_all( SET<STRING> v_type_set, SET<STRING> e_type_set, SET<STRING> reverse_e_type_set,
STRING weight_attribute, STRING label, INT top_k, BOOL print_results = TRUE,
STRING file_path = "", STRING attr = "")
Parameters
Parameter | Description | Default Value |
---|---|---|
|
The vertex types to calculate the distance for. |
(empty set of strings) |
|
The edge types to use |
(empty set of strings) |
|
The reverse edge types to use |
(empty set of strings) |
|
If not empty, use this edge attribute as the edge weight. |
(empty string) |
|
If not empty, read an existing label from this attribute. |
(empty string) |
|
The number of nearest neighbors to consider |
N/A |
|
If true, print output in JSON format to the standard output. |
True |
|
If not empty, write output to this file. |
(empty string) |
|
If not empty, store the predicted label to this vertex attribute. |
(empty string) |
Example
For the movie graph shown in the single vertex version, run knn_cosine_all, using topK=3. Then you get the following result:
{
"Source": [
{
"v_id": "Jing",
"v_type": "Person",
"attributes": {
"name": "Jing",
"known_label": "",
"predicted_label": "",
"@predicted_label": "a"
}
},
{
"v_id": "Neil",
"v_type": "Person",
"attributes": {
"name": "Neil",
"known_label": "",
"predicted_label": "",
"@predicted_label": "b"
}
},
{
"v_id": "Elena",
"v_type": "Person",
"attributes": {
"name": "Elena",
"known_label": "",
"predicted_label": "",
"@predicted_label": ""
}
}
]
}
]