spring_layout

spring_layout(G, dim=2, k=None, pos=None, fixed=None, iterations=50, weight='weight', scale=1.0)

Position nodes using Fruchterman-Reingold force-directed algorithm.

Parameters:

G : NetworkX graph

dim : int

Dimension of layout

k : float (default=None)

Optimal distance between nodes. If None the distance is set to 1/sqrt(n) where n is the number of nodes. Increase this value to move nodes farther apart.

pos : dict or None optional (default=None)

Initial positions for nodes as a dictionary with node as keys and values as a list or tuple. If None, then nuse random initial positions.

fixed : list or None optional (default=None)

Nodes to keep fixed at initial position.

iterations : int optional (default=50)

Number of iterations of spring-force relaxation

weight : string or None optional (default=’weight’)

The edge attribute that holds the numerical value used for the edge weight. If None, then all edge weights are 1.

scale : float (default=1.0)

Scale factor for positions. The nodes are positioned in a box of size [0,scale] x [0,scale].

Returns:

dict :

A dictionary of positions keyed by node

Examples

>>> G=nx.path_graph(4)
>>> pos=nx.spring_layout(G)

# The same using longer function name >>> pos=nx.fruchterman_reingold_layout(G)