cugraph.jaccard(input_graph: Graph, vertex_pair: Optional[DataFrame] = None, use_weight: bool = False)[source]#

Compute the Jaccard similarity between each pair of vertices connected by an edge, or between arbitrary pairs of vertices specified by the user. Jaccard similarity is defined between two sets as the ratio of the volume of their intersection divided by the volume of their union. In the context of graphs, the neighborhood of a vertex is seen as a set. The Jaccard similarity weight of each edge represents the strength of connection between vertices based on the relative similarity of their neighbors.

cugraph.jaccard, in the absence of a specified vertex pair list, will compute the two_hop_neighbors of the entire graph to construct a vertex pair list and will return the jaccard coefficient for those vertex pairs. This is not advisable as the vertex_pairs can grow exponentially with respect to the size of the datasets.


cuGraph Graph instance, should contain the connectivity information as an edge list. The graph should be undirected where an undirected edge is represented by a directed edge in both direction.The adjacency list will be computed if not already present.

This implementation only supports undirected, non-multi Graphs.

vertex_paircudf.DataFrame, optional (default=None)

A GPU dataframe consisting of two columns representing pairs of vertices. If provided, the jaccard coefficient is computed for the given vertex pairs. If the vertex_pair is not provided then the current implementation computes the jaccard coefficient for all adjacent vertices in the graph.

use_weightbool, optional (default=False)

Flag to indicate whether to compute weighted jaccard (if use_weight==True) or un-weighted jaccard (if use_weight==False). ‘input_graph’ must be weighted if ‘use_weight=True’.


GPU data frame of size E (the default) or the size of the given pairs (first, second) containing the Jaccard weights. The ordering is relative to the adjacency list, or that given by the specified vertex pairs.


The first vertex ID of each pair (will be identical to first if specified).


The second vertex ID of each pair (will be identical to second if specified).


The computed Jaccard coefficient between the first and the second vertex ID.


>>> from cugraph.datasets import karate
>>> from cugraph import jaccard
>>> input_graph = karate.get_graph(download=True, ignore_weights=True)
>>> df = jaccard(input_graph)