networkx.algorithms.link_analysis.pagerank_alg.pagerank_scipy

networkx.algorithms.link_analysis.pagerank_alg.pagerank_scipy(G, alpha=0.84999999999999998, personalization=None, max_iter=100, tol=9.9999999999999995e-07)

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.

max_iter : integer, optional

Maximum number of iterations in power method eigenvalue solver.

tol : float, optional

Error tolerance used to check convergence in power method solver.

Returns :

pagerank : dictionary

Dictionary of nodes with PageRank as value

See also

pagerank, pagerank_numpy, google_matrix

Notes

The eigenvector calculation uses power iteration with a SciPy sparse matrix representation.

References

[R133]

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

[R134]

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_scipy(G,alpha=0.9)