networkx.algorithms.link_prediction.jaccard_coefficient¶
-
jaccard_coefficient
(G, ebunch=None)[source]¶ Compute the Jaccard coefficient of all node pairs in ebunch.
Jaccard coefficient of nodes
u
andv
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: - G (graph) – A NetworkX undirected graph.
- ebunch (iterable 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 non-existent edges in the graph will be used. Default value: None.
Returns: piter – 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.
Return type: iterator
Examples
>>> import networkx as nx >>> G = nx.complete_graph(5) >>> preds = nx.jaccard_coefficient(G, [(0, 1), (2, 3)]) >>> for u, v, p in preds: ... '(%d, %d) -> %.8f' % (u, v, p) ... '(0, 1) -> 0.60000000' '(2, 3) -> 0.60000000'
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