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pagerank_scipy

pagerank_numpy

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

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

[R194]A. Langville and C. Meyer, “A survey of eigenvector methods of web information retrieval.” http://citeseer.ist.psu.edu/713792.html
[R195]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)