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- degree_pearson_correlation_coefficient(G, x='out', y='in', weight=None, nodes=None)¶
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.
G : NetworkX 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.
r : float
Assortativity of graph by degree.
This calls scipy.stats.pearsonr.
[R150] M. E. J. Newman, Mixing patterns in networks Physical Review E, 67 026126, 2003 [R151] Foster, J.G., Foster, D.V., Grassberger, P. & Paczuski, M. Edge direction and the structure of networks, PNAS 107, 10815-20 (2010).
>>> G=nx.path_graph(4) >>> r=nx.degree_pearson_correlation_coefficient(G) >>> print("%3.1f"%r) -0.5