Similarity Algorithms
The Graph Data Science Library provides two types of similarity algorithms or functions:
Neighborhood Similarity Algorithms
Two vertices are neighbors of one another if they are directly connected by an edge. The neighborhood of a vertex is its set of neighbors.
Neighborhood similarity algorithms measure the degree to which the neighborhoods of vertices contain the same or similar members. If two vertices have exactly the same neighbors, then they have perfect neighborhood similarity.
The TigerGraph Graph Data Science Library provides the following neighborhood similarity algorithms:

Cosine Similarity of Neighborhoods

Jaccard Similarity of Neighborhoods
Vector Similarity Functions
The TigerGraph Graph Data Science Library also provides vector similarity functions. The vectors can be either LIST
type attributes or inquery ListAccum
accumulators.
The functions are implemented as options of one master function, tg_similarity_accum(VectorA, VectorB, function_specifier_string)
.
Because they are functions, they are documented in the GSQL Reference Manual section on Vector Functions.
