common_neighbor_centrality#

common_neighbor_centrality(G, ebunch=None, alpha=0.8)[source]#

Return the CCPA score for each pair of nodes.

Compute the Common Neighbor and Centrality based Parameterized Algorithm(CCPA) score of all node pairs in ebunch.

CCPA score of u and v is defined as

\[\alpha \cdot (|\Gamma (u){\cap }^{}\Gamma (v)|)+(1-\alpha )\cdot \frac{N}{{d}_{uv}}\]

where \(\Gamma(u)\) denotes the set of neighbors of \(u\), \(\Gamma(v)\) denotes the set of neighbors of \(v\), \(\alpha\) is parameter varies between [0,1], \(N\) denotes total number of nodes in the Graph and \({d}_{uv}\) denotes shortest distance between \(u\) and \(v\).

This algorithm is based on two vital properties of nodes, namely the number of common neighbors and their centrality. Common neighbor refers to the common nodes between two nodes. Centrality refers to the prestige that a node enjoys in a network.

See also

common_neighbors()

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.
alphaParameter defined for participation of Common Neighbor: and Centrality Algorithm share. Values for alpha should normally be between 0 and 1. Default value set to 0.8 because author found better performance at 0.8 for all the dataset. Default value: 0.8

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 Common Neighbor and Centrality based Parameterized Algorithm(CCPA) score.

Raises:

NetworkXNotImplemented: If G is a DiGraph, a Multigraph or a MultiDiGraph.
NetworkXAlgorithmError: If self loops exist in ebunch or in G (if ebunch is None).
NodeNotFound: If ebunch has a node that is not in G.

References

[1]

Ahmad, I., Akhtar, M.U., Noor, S. et al. Missing Link Prediction using Common Neighbor and Centrality based Parameterized Algorithm. Sci Rep 10, 364 (2020). https://doi.org/10.1038/s41598-019-57304-y

Examples

>>> G = nx.complete_graph(5)
>>> preds = nx.common_neighbor_centrality(G, [(0, 1), (2, 3)])
>>> for u, v, p in preds:
...     print(f"({u}, {v}) -> {p}")
(0, 1) -> 3.4000000000000004
(2, 3) -> 3.4000000000000004
----

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 common neighbor centrality 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]