hits#

Returns HITS hubs and authorities values for nodes.

The HITS algorithm computes two numbers for a node. Authorities estimates the node value based on the incoming links. Hubs estimates the node value based on outgoing links.

Parameters:
Ggraph

A NetworkX graph

max_iterinteger, optional

Maximum number of iterations in power method.

tolfloat, optional

Error tolerance used to check convergence in power method iteration.

nstartdictionary, optional

Starting value of each node for power method iteration.

normalizedbool (default=True)

Normalize results by the sum of all of the values.

Returns:
(hubs,authorities)two-tuple of dictionaries

Two dictionaries keyed by node containing the hub and authority values.

Raises:
PowerIterationFailedConvergence

If the algorithm fails to converge to the specified tolerance within the specified number of iterations of the power iteration method.

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 HITS algorithm was designed for directed graphs but this algorithm does not check if the input graph is directed and will execute on undirected graphs.

References

[1]

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

[2]

Jon Kleinberg, Authoritative sources in a hyperlinked environment Journal of the ACM 46 (5): 604-32, 1999. doi:10.1145/324133.324140. http://www.cs.cornell.edu/home/kleinber/auth.pdf.

Examples

>>> G = nx.path_graph(4)
>>> h, a = nx.hits(G)

Additional backends implement this function

cugraphGPU-accelerated backend.
Extra parameters:
dtypedtype or None, optional

The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.

weightstring or None, optional (default=”weight”)

The edge attribute to use as the edge weight.

graphblas : OpenMP-enabled sparse linear algebra backend.