Jaccard Similarity of Neighborhoods (Batch)
".Supported Graph Characteristics
This algorithm computes the same similarity scores as the Jaccard similarity of neighborhoods, single source.
Instead of selecting a single source vertex, however, it calculates similarity scores for all vertex pairs in the graph 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.
Specifications
CREATE QUERY tg_jaccard_nbor_ap_batch ( INT top_k = 10, SET<STRING> v_type,
SET<STRING> feat_v_type, SET<STRING> e_type, SET<STRING> re_type,
STRING similarity_edge, INT src_batch_num = 50, INT nbor_batch_num = 10,
BOOL print_accum = true, INT print_limit = 50, STRING file_path = "")
Parameters
Name  Description 


Number of top scores to report for each vertex 

Vertex type to calculate similarity for 

Feature vertex type 

Directed edge type to traverse 

Reverse edge type to traverse 

If provided, the similarity scores will be saved to this edge type 

Number of batches to split the source vertices into 

Number of batches to split the 2hop neighbor vertices into 

If 

Number of source vertices to print, 1 to print all 

If a file path is provided, the algorithm will output to a file specified by the file path in CSV format 
Time complexity
This algorithm has a time complexity of \$O(E)\$, where \$E\$ is the number of edges.
Output
The result contains the top k Jaccard similarity scores for each vertex and its corresponding pair. A pair is only included if its similarity is greater than 0, meaning there is at least one common neighbor between the pair. The result is available in JSON format, or can be output to a file in CSV, or it can be saved as an edge on the graph itself. A JSON formatted result could look like this:
// Run jaccard_batch on social10 graph traversing through Friend edges
[
{
"Start": [
{
"attributes": {
"Start.@heap": [
{
"val": 0.33333,
"ver": "Howard"
},
{
"val": 0.25,
"ver": "Ivy"
},
{
"val": 0.25,
"ver": "George"
}
]
},
"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": 0.5,
"ver": "Damon"
}
]
},
"v_id": "Chase",
"v_type": "Person"
},
{
"attributes": {
"Start.@heap": [
{
"val": 0.5,
"ver": "Chase"
}
]
},
"v_id": "Damon",
"v_type": "Person"
},
{
"attributes": {
"Start.@heap": [
{
"val": 0.33333,
"ver": "Ivy"
}
]
},
"v_id": "Alex",
"v_type": "Person"
},
{
"attributes": {
"Start.@heap": [
{
"val": 0.5,
"ver": "Howard"
},
{
"val": 0.25,
"ver": "Fiona"
}
]
},
"v_id": "George",
"v_type": "Person"
},
{
"attributes": {
"Start.@heap": []
},
"v_id": "Eddie",
"v_type": "Person"
},
{
"attributes": {
"Start.@heap": [
{
"val": 0.33333,
"ver": "Alex"
},
{
"val": 0.25,
"ver": "Fiona"
}
]
},
"v_id": "Ivy",
"v_type": "Person"
},
{
"attributes": {
"Start.@heap": [
{
"val": 0.5,
"ver": "George"
},
{
"val": 0.33333,
"ver": "Fiona"
}
]
},
"v_id": "Howard",
"v_type": "Person"
}
]
}
]