gn_graph(n, kernel=None, create_using=None, seed=None)
The GN graph is built by adding nodes one at a time with a link to one previously added node. The target node for the link is chosen with probability based on degree. The default attachment kernel is a linear function of the degree of a node.
The graph is always a (directed) tree.
The number of nodes for the generated graph.
The attachment kernel.
Graph type to create. If graph instance, then cleared before populated.
Indicator of random number generation state. See Randomness<randomness>
.
Returns the growing network (GN) digraph with n nodes.
>>> D = nx.gn_graph(10) # the GN graph
... G = D.to_undirected() # the undirected version
>>> D = nx.gn_graph(10, kernel=lambda x: x ** 1.5) # A_k = k^1.5See :
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.readwrite.json_graph.node_link.node_link_data
networkx.generators.directed.gn_graph
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