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 – Assortativity of graph by degree.
- Return type
>>> G = nx.path_graph(4) >>> r = nx.degree_pearson_correlation_coefficient(G) >>> print("%3.1f"%r) -0.5
This calls scipy.stats.pearsonr.
M. E. J. Newman, Mixing patterns in networks Physical Review E, 67 026126, 2003
Foster, J.G., Foster, D.V., Grassberger, P. & Paczuski, M. Edge direction and the structure of networks, PNAS 107, 10815-20 (2010).