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degree_assortativity_coefficient¶
- degree_assortativity_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.
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 : float
Assortativity of graph by degree.
See also
attribute_assortativity_coefficient, numeric_assortativity_coefficient, neighbor_connectivity, degree_mixing_dict, degree_mixing_matrix
Notes
This computes Eq. (21) in Ref. [R148] , 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 user-specified degree type for the source and target.
References
[R148] (1, 2) M. E. J. Newman, Mixing patterns in networks, Physical Review E, 67 026126, 2003 [R149] 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_assortativity_coefficient(G) >>> print("%3.1f"%r) -0.5