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:

Notes

This calls scipy.stats.pearsonr.

Returns nan if the graph has fewer than two (degree, degree) pairs, since the Pearson correlation coefficient is undefined in that case.

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