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

SimRank is a similarity metric that says "two objects are considered to be similar if they are referenced by similar objects." .

The pseudo-code definition from the paper is:

def simrank(G, u, v):
    in_neighbors_u = G.predecessors(u)
    in_neighbors_v = G.predecessors(v)
    scale = C / (len(in_neighbors_u) * len(in_neighbors_v))
    return scale * sum(simrank(G, w, x)
                       for w, x in product(in_neighbors_u,
                                           in_neighbors_v))

where G is the graph, u is the source, v is the target, and C is a float decay or importance factor between 0 and 1.

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 : dictionary or float

If source and target are both None , this returns a dictionary of dictionaries, where keys are node pairs and value are similarity of the pair of nodes.

If source is not None but target is, this returns a dictionary mapping node to the similarity of source and that node.

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

Returns the SimRank similarity of nodes in the graph G .

Examples

>>> G = nx.cycle_graph(2)
... nx.simrank_similarity(G) {0: {0: 1.0, 1: 0.0}, 1: {0: 0.0, 1: 1.0}}
>>> nx.simrank_similarity(G, source=0)
{0: 1.0, 1: 0.0}
>>> nx.simrank_similarity(G, source=0, target=0)
1.0

The result of this function can be converted to a numpy array representing the SimRank matrix by using the node order of the graph to determine which row and column represent each node. Other ordering of nodes is also possible.

>>> import numpy as np
... sim = nx.simrank_similarity(G)
... np.array([[sim[u][v] for v in G] for u in G]) array([[1., 0.], [0., 1.]])
>>> sim_1d = nx.simrank_similarity(G, source=0)
... np.array([sim[0][v] for v in G]) array([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

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#1211
type: <class 'function'>
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