Note

This documents the development version of NetworkX. Documentation for the current release can be found here.

# networkx.algorithms.similarity.simrank_similarity_numpy¶

`simrank_similarity_numpy`(G, source=None, target=None, importance_factor=0.9, max_iterations=100, tolerance=0.0001)[source]

Calculate SimRank of nodes in `G` using matrices with `numpy`.

The SimRank algorithm for determining node similarity is defined in [1].

Parameters
GNetworkX graph

A NetworkX graph

sourcenode

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

targetnode

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_factorfloat

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

max_iterationsinteger

Maximum number of iterations.

tolerancefloat

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
similaritynumpy matrix, numpy array or float

If `source` and `target` are both `None`, this returns a Matrix containing SimRank scores of the nodes.

If `source` is not `None` but `target` is, this returns an 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.

References

1

G. Jeh and J. Widom. “SimRank: a measure of structural-context similarity”, In KDD’02: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 538–543. ACM Press, 2002.

Examples

```>>> G = nx.cycle_graph(4)
>>> sim = nx.simrank_similarity_numpy(G)
```