networkx.drawing.layout.spring_layout¶

spring_layout
(G, k=None, pos=None, fixed=None, iterations=50, threshold=0.0001, weight='weight', scale=1, center=None, dim=2, seed=None)¶ Position nodes using FruchtermanReingold forcedirected algorithm.
The algorithm simulates a forcedirected representation of the network treating edges as springs holding nodes close, while treating nodes as repelling objects, sometimes called an antigravity force. Simulation continues until the positions are close to an equilibrium.
There are some hardcoded values: minimal distance between nodes (0.01) and “temperature” of 0.1 to ensure nodes don’t fly away. During the simulation,
k
helps determine the distance between nodes, thoughscale
andcenter
determine the size and place after rescaling occurs at the end of the simulation.Fixing some nodes doesn’t allow them to move in the simulation. It also turns off the rescaling feature at the simulation’s end. In addition, setting
scale
toNone
turns off rescaling. Parameters
G (NetworkX graph or list of nodes) – A position will be assigned to every node in G.
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 coordinate list or tuple. If None, then use random initial positions.
fixed (list or None optional (default=None)) – Nodes to keep fixed at initial position. ValueError raised if
fixed
specified andpos
not.iterations (int optional (default=50)) – Maximum number of iterations taken
threshold (float optional (default = 1e4)) – Threshold for relative error in node position changes. The iteration stops if the error is below this threshold.
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 (number or None (default: 1)) – Scale factor for positions. Not used unless
fixed is None
. If scale is None, no rescaling is performed.center (arraylike or None) – Coordinate pair around which to center the layout. Not used unless
fixed is None
.dim (int) – Dimension of layout.
seed (int, RandomState instance or None optional (default=None)) – Set the random state for deterministic node layouts. If int,
seed
is the seed used by the random number generator, if numpy.random.RandomState instance,seed
is the random number generator, if None, the random number generator is the RandomState instance used by numpy.random.
 Returns
pos – A dictionary of positions keyed by node
 Return type
Examples
>>> G = nx.path_graph(4) >>> pos = nx.spring_layout(G)
# The same using longer but equivalent function name >>> pos = nx.fruchterman_reingold_layout(G)