Cosine Similarity of Neighborhoods (Batch)
This algorithm computes the same similarity scores as the Cosine similarity of neighborhoods, all pairs algorithm except that it starts from all of the vertices as the source vertex and computes its similarity scores with its neighbors for all the vertices in parallel.
Since this is a memoryintensive operation, it is split into batches to reduce peak memory usage. The user can specify how many batches it is to be split into. Compared with the Cosine similarity of neighborhoods, all pairs algorithm, this algorithm allows you to split the workload into multiple batches and reduces the burden on memory.
This algorithm has a time complexity of O(E), where E is the number of edges, and runs on graphs with weighted edges (directed or undirected).
Specifications
tg_cosine_batch(STRING vertex_type, STRING edge_type, STRING edge_attribute,
INT topK, BOOL print_accum = true, STRING file_path,
STRING similarity_edge, INT num_of_batches=1)
Parameters
Name  Description 


Vertex type to calculate similarity for 

Directed edge type to traverse 

Name of the attribute on the edge type to use as the weight 

Number of top scores to report for each vertex 

If 

If provided, the similarity score will be saved to this edge. 

If not empty, write output to this file in CSV. 

Number of batches to divide the query into 
Result
The result of this algorithm is the top k cosine similarity scores and their corresponding pair for each vertex. The score is only included if it is greater than 0.
The result can be output in JSON format, in CSV to a file, or saved as a similarity edge in the graph itself.
Example
Using the social10
graph, we can calculate the cosine similarity of every person to every other person connected by the Friend
edge, and print out the top k most similar pairs for each vertex.
GSQL > RUN QUERY tg_cosine_batch("Person", "Friend", "weight", 5, true, "", "", 1)
// Every vertex and their most similar pairs ranked by their Cosine
// Similarity score.
[
{
"start": [
{
"attributes": {
"start.@heap": [
{
"val": 0.49903,
"ver": "Howard"
},
{
"val": 0.43938,
"ver": "George"
},
{
"val": 0.05918,
"ver": "Alex"
},
{
"val": 0.05579,
"ver": "Ivy"
}
]
},
"v_id": "Fiona",
"v_type": "Person"
},
{
"attributes": {
"start.@heap": []
},
"v_id": "Justin",
"v_type": "Person"
},
{
"attributes": {
"start.@heap": []
},
"v_id": "Bob",
"v_type": "Person"
},
{
"attributes": {
"start.@heap": [
{
"val": 0.22361,
"ver": "Bob"
},
{
"val": 0.21213,
"ver": "Alex"
}
]
},
"v_id": "Chase",
"v_type": "Person"
},
{
"attributes": {
"start.@heap": [
{
"val": 0.57143,
"ver": "Bob"
},
{
"val": 0.12778,
"ver": "Chase"
}
]
},
"v_id": "Damon",
"v_type": "Person"
},
{
"attributes": {
"start.@heap": []
},
"v_id": "Alex",
"v_type": "Person"
},
{
"attributes": {
"start.@heap": [
{
"val": 0.64253,
"ver": "Alex"
},
{
"val": 0.63607,
"ver": "Ivy"
},
{
"val": 0.27091,
"ver": "Howard"
},
{
"val": 0.14364,
"ver": "Fiona"
}
]
},
"v_id": "George",
"v_type": "Person"
},
{
"attributes": {
"start.@heap": []
},
"v_id": "Eddie",
"v_type": "Person"
},
{
"attributes": {
"start.@heap": [
{
"val": 0.94848,
"ver": "Fiona"
},
{
"val": 0.6364,
"ver": "Alex"
},
{
"val": 0.31046,
"ver": "George"
},
{
"val": 0.1118,
"ver": "Howard"
}
]
},
"v_id": "Ivy",
"v_type": "Person"
},
{
"attributes": {
"start.@heap": [
{
"val": 1.09162,
"ver": "Fiona"
},
{
"val": 0.78262,
"ver": "Ivy"
},
{
"val": 0.11852,
"ver": "George"
}
]
},
"v_id": "Howard",
"v_type": "Person"
}
]
}
]