networkx.algorithms.assortativity.degree_assortativity_coefficient¶

degree_assortativity_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.
 Parameters
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 degree assortativity only for nodes in container. The default is all nodes.
 Returns
r – Assortativity of graph by degree.
 Return type
Examples
>>> G = nx.path_graph(4) >>> r = nx.degree_assortativity_coefficient(G) >>> print(f"{r:3.1f}") 0.5
See also
attribute_assortativity_coefficient()
,numeric_assortativity_coefficient()
,neighbor_connectivity()
,degree_mixing_dict()
,degree_mixing_matrix()
Notes
This computes Eq. (21) in Ref. 1 , where e is the joint probability distribution (mixing matrix) of the degrees. If G is directed than the matrix e is the joint probability of the userspecified degree type for the source and target.
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, 1081520 (2010).