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jaccard_coefficient¶

jaccard_coefficient
(G, ebunch=None)[source]¶ Compute the Jaccard coefficient of all node pairs in ebunch.
Jaccard coefficient of nodes and is defined as
where denotes the set of neighbors of .
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 2tuples (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: piter – An iterator of 3tuples 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. LibenNowell, J. Kleinberg. The Link Prediction Problem for Social Networks (2004). http://www.cs.cornell.edu/home/kleinber/linkpred.pdf