cugraph.sorensen_coefficient#

cugraph.sorensen_coefficient(G: Union[Graph, Graph], ebunch: Optional[Union[DataFrame, Iterable[Union[int, str, float]]]] = None, do_expensive_check: bool = False)[source]#

Compute sorensen coefficient.

Parameters:

Gcugraph.Graph or NetworkX.Graph

cuGraph or NetworkX 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.

ebunchcudf.DataFrame or iterable of node pairs, optional (default=None)

A GPU dataframe consisting of two columns representing pairs of vertices or iterable of 2-tuples (u, v) where u and v are nodes in the graph.

If provided, the Overlap coefficient is computed for the given vertex pairs. Otherwise, the current implementation computes the overlap coefficient for all adjacent vertices in the graph.

do_expensive_checkbool, optional (default=False)

Deprecated. This option added a check to ensure integer vertex IDs are sequential values from 0 to V-1. That check is now redundant because cugraph unconditionally renumbers and un-renumbers integer vertex IDs for optimal performance, therefore this option is deprecated and will be removed in a future version.

Returns:

dfcudf.DataFrame

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

df[‘first’]cudf.Series: The first vertex ID of each pair (will be identical to first if specified).
df[‘second’]cudf.Series: The second vertex ID of each pair (will be identical to second if specified).
df[‘sorensen_coeff’]cudf.Series: The computed Sorensen coefficient between the first and the second vertex ID.

Examples

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