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:
- GNetworkX graph
- x: string (â€˜inâ€™,â€™outâ€™)
The degree type for source node (directed graphs only).
- y: string (â€˜inâ€™,â€™outâ€™)
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:
- rfloat
Assortativity of graph by degree.
See also
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 user-specified 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, 10815-20 (2010).
Examples
>>> G = nx.path_graph(4) >>> r = nx.degree_assortativity_coefficient(G) >>> print(f"{r:3.1f}") -0.5