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Compute the resource allocation index of all node pairs in ebunch using community information.

For two nodes \(u\) and \(v\), this function computes the resource allocation index considering only common neighbors belonging to the same community as \(u\) and \(v\). Mathematically,

\[\sum_{w \in \Gamma(u) \cap \Gamma(v)} \frac{f(w)}{|\Gamma(w)|}\]

where \(f(w)\) equals 1 if \(w\) belongs to the same community as \(u\) and \(v\) or 0 otherwise and \(\Gamma(u)\) denotes the set of neighbors of \(u\).


G : graph

A NetworkX undirected graph.

ebunch : iterable of node pairs, optional (default = None)

The score 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.

community : string, optional (default = ‘community’)

Nodes attribute name containing the community information. G[u][community] identifies which community u belongs to. Each node belongs to at most one community. Default value: ‘community’.


piter : iterator

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


[R271]Sucheta Soundarajan and John Hopcroft. Using community information to improve the precision of link prediction methods. In Proceedings of the 21st international conference companion on World Wide Web (WWW ‘12 Companion). ACM, New York, NY, USA, 607-608. http://doi.acm.org/10.1145/2187980.2188150


>>> import networkx as nx
>>> G = nx.Graph()
>>> G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3)])
>>> G.node[0]['community'] = 0
>>> G.node[1]['community'] = 0
>>> G.node[2]['community'] = 1
>>> G.node[3]['community'] = 0
>>> preds = nx.ra_index_soundarajan_hopcroft(G, [(0, 3)])
>>> for u, v, p in preds:
...     '(%d, %d) -> %.8f' % (u, v, p)
'(0, 3) -> 0.50000000'