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

Position nodes using Fruchterman-Reingold force-directed algorithm.

  • G (NetworkX graph or list of nodes) –
  • 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 use 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].
center : array-like or None
Coordinate pair around which to center the layout.
Returns:A dictionary of positions keyed by node
Return type:dict


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

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