dynamicalab.drawing.clustered_layout

dynamicalab.drawing.clustered_layout(G, groups, centroids, radius_scale=None)

This function constructs a layout where nodes are sampled in circles around centroids of groups.

Parameters

G : Networkx Graph

A Networkx Graph or DiGraph.

groups : list of lists

A list of groups of nodes. Each element of groups must be a list of node_id.

centroids : list of lists

Position of the centers of the groups

radius_scale : list of float : (default=None)

Scaling of the radius for each group. If None, then the scaling is equal to 1 for all groups. Smaller scaling increases the nodes density.

Returns

dict

Dictionary of positions that can be use directly with draw_networks. Each key is a node_id (from groups) and the value is a position [x,y].

list of edges

Split of edges between each group. Makes the plot much simpler. Check the example.

Raise

ValueError

Occurs if groups does not contain all nodes (low criterion).

Example

import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
import dynamicalab.drawing as draw

# Generate a network
pin = [0.05, 0.2]
pout= 0.003
sizes = [100,250]
G = nx.random_partition_graph(sizes, 0.25, 0.01)

node_subsets = [range(0,sizes[0]), range(sizes[0], sizes[0]+sizes[1])]
centroids = [(0,0), (4,4)]
radius_scale = [0.02,0.03]

# Get layout
pos, edge_bunchs = draw.clustered_layout(G, node_subsets, centroids, radius_scale)

# Draw
plt.figure(figsize=(10,10))

node_colors = ['#b2182b', '#2166ac']
for i, nodes in enumerate(node_subsets):
    nx.draw_networkx_nodes(G, pos=pos, node_size=30, nodelist=nodes, node_color=node_colors[i])

colors = ['#bdbdbd', '#bdbdbd', '#67a9cf', '#ef8a62']
for i, edges in enumerate(edge_bunchs):
    nx.draw_networkx_edges(G, pos=pos, alpha=0.2, edgelist=edges, edge_color=colors[i])
    
plt.axis("off")