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
• 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 – 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.

Return type

numpy matrix, numpy array or float

Examples

>>> from numpy import array
>>> G = nx.cycle_graph(4)
>>> sim = nx.simrank_similarity_numpy(G)


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.