pagerank_scipy#
- pagerank_scipy(G, alpha=0.85, personalization=None, max_iter=100, tol=1e-06, nstart=None, weight='weight', dangling=None)[source]#
Returns 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:
- Ggraph
A NetworkX graph. Undirected graphs will be converted to a directed graph with two directed edges for each undirected edge.
- alphafloat, optional
Damping parameter for PageRank, default=0.85.
- personalization: dict, optional
The “personalization vector” consisting of a dictionary with a key some subset of graph nodes and personalization value each of those. At least one personalization value must be non-zero. If not specfiied, a nodes personalization value will be zero. By default, a uniform distribution is used.
- max_iterinteger, optional
Maximum number of iterations in power method eigenvalue solver.
- tolfloat, optional
Error tolerance used to check convergence in power method solver.
- nstartdictionary, optional
Starting value of PageRank iteration for each node.
- weightkey, optional
Edge data key to use as weight. If None weights are set to 1.
- dangling: dict, optional
The outedges to be assigned to any “dangling” nodes, i.e., nodes without any outedges. The dict key is the node the outedge points to and the dict value is the weight of that outedge. By default, dangling nodes are given outedges according to the personalization vector (uniform if not specified) This must be selected to result in an irreducible transition matrix (see notes under google_matrix). It may be common to have the dangling dict to be the same as the personalization dict.
- Returns:
- pagerankdictionary
Dictionary of nodes with PageRank as value
- Raises:
- PowerIterationFailedConvergence
If the algorithm fails to converge to the specified tolerance within the specified number of iterations of the power iteration method.
See also
Notes
The eigenvector calculation uses power iteration with a SciPy sparse matrix representation.
This implementation works with Multi(Di)Graphs. For multigraphs the weight between two nodes is set to be the sum of all edge weights between those nodes.
References
[1]A. Langville and C. Meyer, “A survey of eigenvector methods of web information retrieval.” http://citeseer.ist.psu.edu/713792.html
[2]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)