Jaccard Similarity of Neighborhoods (All Pairs)
This algorithm computes the same similarity scores as the Jaccard similarity of neighborhoods, singlesource algorithm, except that it considers ALL pairs of vertices in the graph (for the vertex and edge types selected by the user). Naturally, this algorithm will take longer to run. For very large and very dense graphs, this algorithm may not be a practical choice.
Specifications
tg_jaccard_nbor_ap(STRING v_type, STRING e_type, STRING re_type, INT top_k,
BOOL print_accum = TRUE, STRING similarity_edge = "", STRING file_path = "")
Characteristic  Value 

Result 
The top k vertex pairs in the graph that have the highest similarity scores, along with their scores. The result is available in three forms:

Input Parameters 

Result Size 

Time Complexity 
O(E^2 / V), V = number of vertices, E = number of edges 
Graph Types 
Undirected or directed edges, unweighted edges 
The algorithm will not output more than K vertex pairs, so the algorithm may arbitrarily chose to output one vertex pair over another, if there are tied similarity scores.
Example
For the movie graph, calculate the Jaccard similarity between all pairs and show the 5 most similar pairs: jaccard_nbor_ap(5). This is the JSON output :
[
{
"@@total_result": [
{
"vertex1": "Kat",
"vertex2": "Neil",
"score": 0.5
},
{
"vertex1": "Kevin",
"vertex2": "Neil",
"score": 0.4
},
{
"vertex1": "Jing",
"vertex2": "Alex",
"score": 0.25
},
{
"vertex1": "Kat",
"vertex2": "Kevin",
"score": 0.25
},
{
"vertex1": "Jing",
"vertex2": "Neil",
"score": 0.2
}
]
}
]