Question

我有一个涉及图形的可视化问题。我有N个节点，它们属于某些M网络。节点可以具有网络间边缘（在同一网络内）和网络内边缘（从一个网络中的节点到另一个网络的边缘）。

当我在networkx中可视化图形时，我正在寻找一种将网络放置/群集在一起的方法，以便可以轻松地识别出内部/内部网络连接。因此，理想情况下，所有蓝色节点都应作为网络群集在一起（没有特定顺序）。橙色或绿色也是如此。

通过我不尝试查找集线器/群集的方式，我知道哪些节点位于哪个网络中，我只是在尝试寻找一种可视化它的方法。有一些简单的方法可以做到这一点吗？诸如高级弹簧布局之类的东西，无论边缘权重/弹簧力如何，我都可以指定一些节点一起出现？

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import string, random
import networkx as nx
import matplotlib.pyplot as plt
from scipy.sparse import random as sparse_random


# Random string generator
def rand_string(size=6, chars=string.ascii_uppercase):
    return ''.join(random.choice(chars) for _ in range(size))


# Set up a nodes and networks randomly
nodes = [rand_string() for _ in range(30)]
networks = [rand_string() for _ in range(5)]
networks_list = networks*6
random.shuffle(networks_list)

# Define what nodes belong to what network and what their color should be
node_network_map = dict(zip(nodes, networks_list))
colors = ['green', 'royalblue', 'red', 'orange', 'cyan']
color_map = dict(zip(networks, colors))

graph = nx.Graph()
graph.add_nodes_from(nodes)
nodes_by_color = {val: [node for node in graph if color_map[node_network_map[node]] == val]
                  for val in colors}

# Take random sparse matrix as adjacency matrix
mat = sparse_random(30, 30, density=0.3).todense()
for row, row_val in enumerate(nodes):
    for col, col_val in enumerate(nodes):
        if col > row and mat[row, col] != 0.0: # Stick to upper half triangle, mat is not symmetric
            graph.add_edge(row_val, col_val, weight=mat[row, col])

# Choose a layout to visualize graph
pos = nx.spring_layout(graph)
edges = graph.edges()

# Get the edge weights and normalize them 
weights = [abs(graph[u][v]['weight']) for u, v in edges]
weights_n = [5*float(i)/max(weights) for i in weights] # Change 5 to control thickness

# First draw the nodes 
plt.figure()
for color, node_names in nodes_by_color.items():
    nx.draw_networkx_nodes(graph, pos=pos, nodelist=node_names, node_color=color)

# Then draw edges with thickness defined by weights_n
nx.draw_networkx_edges(graph, pos=pos, width=weights_n)
nx.draw_networkx_labels(graph, pos=pos)
plt.show()

Answer 1

为了获得更好的节点布局，我首先使用圆形布局（替换弹簧布局）。然后，我将每个节点组沿着更大的圆的周长移动到它们的新位置。

# --- Begin_myhack ---
# All this code should replace original `pos=nx.spring_layout(graph)`
import numpy as np
pos = nx.circular_layout(graph)   # replaces your original pos=...
# prep center points (along circle perimeter) for the clusters
angs = np.linspace(0, 2*np.pi, 1+len(colors))
repos = []
rad = 3.5     # radius of circle
for ea in angs:
    if ea > 0:
        #print(rad*np.cos(ea), rad*np.sin(ea))  # location of each cluster
        repos.append(np.array([rad*np.cos(ea), rad*np.sin(ea)]))
for ea in pos.keys():
    #color = 'black'
    posx = 0
    if ea in nodes_by_color['green']:
        #color = 'green'
        posx = 0
    elif ea in nodes_by_color['royalblue']:
        #color = 'royalblue'
        posx = 1
    elif ea in nodes_by_color['red']:
        #color = 'red'
        posx = 2
    elif ea in nodes_by_color['orange']:
        #color = 'orange'
        posx = 3
    elif ea in nodes_by_color['cyan']:
        #color = 'cyan'
        posx = 4
    else:
        pass
    #print(ea, pos[ea], pos[ea]+repos[posx], color, posx)
    pos[ea] += repos[posx]
# --- End_myhack ---

输出图将与此类似：

编辑

通常，没有一种特殊的布局在所有情况下都是最好的。因此，我提供了第二种解决方案，该解决方案使用同心圆来分离节点的各个组。这是相关的代码及其示例输出。

# --- Begin_my_hack ---
# All this code should replace original `pos=nx.spring_layout(graph)`
import numpy as np
pos = nx.circular_layout(graph)
radii = [7,15,30,45,60]  # for concentric circles

for ea in pos.keys():
    new_r = 1
    if ea in nodes_by_color['green']:
        new_r = radii[0]
    elif ea in nodes_by_color['royalblue']:
        new_r = radii[1]
    elif ea in nodes_by_color['red']:
        new_r = radii[2]
    elif ea in nodes_by_color['orange']:
        new_r = radii[3]
    elif ea in nodes_by_color['cyan']:
        new_r = radii[4]
    else:
        pass
    pos[ea] *= new_r   # reposition nodes as concentric circles
# --- End_my_hack ---

在networkx

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1 个答案: