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.
A NetworkX graph. Undirected graphs will be converted to a directed
graph with two directed edges for each undirected edge.
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.
Maximum number of iterations in power method eigenvalue solver.
Error tolerance used to check convergence in power method solver.
Starting value of PageRank iteration for each node.
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.
Dictionary of nodes with PageRank as value
If the algorithm fails to converge to the specified tolerance
within the specified number of iterations of the power iteration
The eigenvector calculation is done by the power iteration method
and has no guarantee of convergence. The iteration will stop after
an error tolerance of len(G)*tol has been reached. If the
number of iterations exceed max_iter, a
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 edge in the
directed graph to two edges.