networkx.algorithms.link_analysis.pagerank_alg.pagerank

networkx.algorithms.link_analysis.pagerank_alg.pagerank(G, alpha=0.84999999999999998, personalization=None, max_iter=100, tol=1e-08, nstart=None)

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.

nstart : dictionary, optional

Starting value of PageRank iteration for each node.

Returns :

pagerank : dictionary

Dictionary of nodes with PageRank as value

See also

pagerank_numpy, pagerank_scipy, google_matrix

Notes

The eigenvector calculation is done by the power iteration method and has no guarantee of convergence. The iteration will stop after max_iter iterations or an error tolerance of number_of_nodes(G)*tol has been reached.

The PageRank algorithm was designed for directed graphs but this algorithm does not check if the input graph is directed and will execute on undirected graphs by converting each oriented edge in the directed graph to two edges.

References

[R129]

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

[R130]

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(G,alpha=0.9)