_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 .
A NetworkX graph
If this is specified, the returned dictionary maps each node v
in the graph to the similarity between source
and v
.
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.
The relative importance of indirect neighbors with respect to direct neighbors.
Maximum number of iterations.
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.
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
.
>>> 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) 1.0See :
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.algorithms.similarity._simrank_similarity_numpy
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