hits_scipy(G, max_iter=100, tol=1e-06, nstart=None, normalized=True)
hits_scipy is deprecated and will be removed in networkx 3.0
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.
This implementation uses SciPy sparse matrices.
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.
A NetworkX graph
Maximum number of iterations in power method.
Error tolerance used to check convergence in power method iteration.
Starting value of each node for power method iteration.
Normalize results by the sum of all of the values.
If the algorithm fails to converge to the specified tolerance within the specified number of iterations of the power iteration method.
Two dictionaries keyed by node containing the hub and authority values.
Returns HITS hubs and authorities values for nodes.
>>> G = nx.path_graph(4)See :
... h, a = nx.hits(G)
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them