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.
>>> 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).