pagerank_numpy

pagerank_numpy(G, alpha=0.84999999999999998, personalization=None, weight='weight')

Return the PageRank of the nodes in the graph.

PageRank computes a ranking of the nodes in the graph G based on the structure of the incoming links. It was originally designed as an algorithm to rank web pages.

Parameters :

G : graph

A NetworkX graph

alpha : float, optional

Damping parameter for PageRank, default=0.85

personalization: dict, optional :

The “personalization vector” consisting of a dictionary with a key for every graph node and nonzero personalization value for each node.

weight : key, optional

Edge data key to use as weight. If None weights are set to 1.

Returns :

pagerank : dictionary

Dictionary of nodes with PageRank as value

See also

pagerank, pagerank_scipy, google_matrix

Notes

The eigenvector calculation uses NumPy’s interface to the LAPACK eigenvalue solvers. This will be the fastest and most accurate for small graphs.

This implementation works with Multi(Di)Graphs.

References

[R178]

A. Langville and C. Meyer, “A survey of eigenvector methods of web information retrieval.” http://citeseer.ist.psu.edu/713792.html

[R179]

Page, Lawrence; Brin, Sergey; Motwani, Rajeev and Winograd, Terry, The PageRank citation ranking: Bringing order to the Web. 1999 http://dbpubs.stanford.edu:8090/pub/showDoc.Fulltext?lang=en&doc=1999-66&format=pdf

Examples

>>> G=nx.DiGraph(nx.path_graph(4))
>>> pr=nx.pagerank_numpy(G,alpha=0.9)