Note

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

# networkx.algorithms.assortativity.degree_pearson_correlation_coefficient¶

`degree_pearson_correlation_coefficient`(G, x='out', y='in', weight=None, nodes=None)[source]

Compute degree assortativity of graph.

Assortativity measures the similarity of connections in the graph with respect to the node degree.

This is the same as degree_assortativity_coefficient but uses the potentially faster scipy.stats.pearsonr function.

Parameters
GNetworkX graph
x: string (‘in’,’out’)

The degree type for source node (directed graphs only).

y: string (‘in’,’out’)

The degree type for target node (directed graphs only).

weight: string or None, optional (default=None)

The edge attribute that holds the numerical value used as a weight. If None, then each edge has weight 1. The degree is the sum of the edge weights adjacent to the node.

nodes: list or iterable (optional)

Compute pearson correlation of degrees only for specified nodes. The default is all nodes.

Returns
rfloat

Assortativity of graph by degree.

Notes

This calls scipy.stats.pearsonr.

References

1

M. E. J. Newman, Mixing patterns in networks Physical Review E, 67 026126, 2003

2

Foster, J.G., Foster, D.V., Grassberger, P. & Paczuski, M. Edge direction and the structure of networks, PNAS 107, 10815-20 (2010).

Examples

```>>> G = nx.path_graph(4)
>>> r = nx.degree_pearson_correlation_coefficient(G)
>>> print(f"{r:3.1f}")
-0.5
```