node_degree_xy(G, x='out', y='in', weight=None, nodes=None)
For undirected graphs each edge is produced twice, once for each edge representation (u, v) and (v, u), with the exception of self-loop edges which only appear once.
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.
Use only edges that are adjacency to specified nodes. The default is all nodes.
Generates 2-tuple of (degree, degree) values.
Generate node degree-degree pairs for edges in G.
>>> G = nx.DiGraph()
... G.add_edge(1, 2)
... list(nx.node_degree_xy(G, x="out", y="in")) [(1, 1)]
>>> list(nx.node_degree_xy(G, x="in", y="out")) [(0, 0)]See :
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.algorithms.assortativity.pairs.node_degree_xy
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