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_simrank_similarity_numpy(G, source=None, target=None, importance_factor=0.9, max_iterations=1000, tolerance=0.0001)

The SimRank algorithm for determining node similarity is defined in .

Parameters

G : NetworkX graph

A NetworkX graph

source : node

If this is specified, the returned dictionary maps each node v in the graph to the similarity between source and v .

target : node

If both source and target are specified, the similarity value between source and target is returned. If target is specified but source is not, this argument is ignored.

importance_factor : float

The relative importance of indirect neighbors with respect to direct neighbors.

max_iterations : integer

Maximum number of iterations.

tolerance : float

Error tolerance used to check convergence. When an iteration of the algorithm finds that no similarity value changes more than this amount, the algorithm halts.

Returns

similarity : numpy array or float

If source and target are both None , this returns a 2D array containing SimRank scores of the nodes.

If source is not None but target is, this returns an 1D array containing SimRank scores of source and that node.

If neither source nor target is None , this returns the similarity value for the given pair of nodes.

Calculate SimRank of nodes in G using matrices with numpy .

Examples

>>> G = nx.cycle_graph(2)
... nx.similarity._simrank_similarity_numpy(G) array([[1., 0.], [0., 1.]])
>>> nx.similarity._simrank_similarity_numpy(G, source=0)
array([1., 0.])
>>> nx.similarity._simrank_similarity_numpy(G, source=0, target=0)
1.0
See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

networkx.algorithms.similarity._simrank_similarity_numpy

Local connectivity graph

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


GitHub : /networkx/algorithms/similarity.py#1393
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