degree_pearson_correlation_coefficient(G, x='out', y='in', weight=None, nodes=None)
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.
This calls scipy.stats.pearsonr.
The degree type for source node (directed graphs only).
The degree type for target node (directed graphs only).
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.
Compute pearson correlation of degrees only for specified nodes. The default is all nodes.
Assortativity of graph by degree.
Compute degree assortativity of graph.
>>> G = nx.path_graph(4)See :
... r = nx.degree_pearson_correlation_coefficient(G)
... print(f"{r:3.1f}") -0.5
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.algorithms.assortativity.correlation.degree_pearson_correlation_coefficient
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them