jaccard_coefficient#

jaccard_coefficient(G, ebunch=None)[source]#

Compute the Jaccard coefficient of all node pairs in ebunch.

Jaccard coefficient of nodes u and v is defined as

\[\frac{|\Gamma(u) \cap \Gamma(v)|}{|\Gamma(u) \cup \Gamma(v)|}\]

where \(\Gamma(u)\) denotes the set of neighbors of \(u\).

Parameters:

Ggraph: A NetworkX undirected graph.
ebunchiterable of node pairs, optional (default = None): Jaccard coefficient will be computed for each pair of nodes given in the iterable. The pairs must be given as 2-tuples (u, v) where u and v are nodes in the graph. If ebunch is None then all nonexistent edges in the graph will be used. Default value: None.

Returns:

piteriterator: An iterator of 3-tuples in the form (u, v, p) where (u, v) is a pair of nodes and p is their Jaccard coefficient.

Raises:

NetworkXNotImplemented: If G is a DiGraph, a Multigraph or a MultiDiGraph.
NodeNotFound: If ebunch has a node that is not in G.

References

[1]

D. Liben-Nowell, J. Kleinberg. The Link Prediction Problem for Social Networks (2004). http://www.cs.cornell.edu/home/kleinber/link-pred.pdf

Examples

>>> G = nx.complete_graph(5)
>>> preds = nx.jaccard_coefficient(G, [(0, 1), (2, 3)])
>>> for u, v, p in preds:
...     print(f"({u}, {v}) -> {p:.8f}")
(0, 1) -> 0.60000000
(2, 3) -> 0.60000000