preferential_attachment#

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

Compute the preferential attachment score of all node pairs in ebunch.

Preferential attachment score of u and v is defined as

\[|\Gamma(u)| |\Gamma(v)|\]

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

Parameters:

Ggraph: NetworkX undirected graph.
ebunchiterable of node pairs, optional (default = None): Preferential attachment 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 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 preferential attachment score.

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.preferential_attachment(G, [(0, 1), (2, 3)])
>>> for u, v, p in preds:
...     print(f"({u}, {v}) -> {p}")
(0, 1) -> 16
(2, 3) -> 16
----

Additional backends implement this function

parallelA networkx backend that uses joblib to run graph algorithms in parallel. Find the nx-parallel’s configuration guide here

The edge pairs are chunked into pairs_chunks and then the preferential attachment for all pairs_chunks is computed in parallel over n_jobs number of CPU cores.

Additional parameters:

get_chunksstr, function (default = “chunks”): A function that takes in a list of all the edges (or ebunch) as input and returns an iterable pairs_chunks. The default chunking is done by slicing ebunch into n_jobs number of chunks.

[Source]