与邻接矩阵成比例缩放网络节点和边

Question

NetworkX是否有一种内置的方式来缩放与邻接矩阵频率/节点节点频率成比例的节点和边？我试图根据邻接矩阵的频率来调整节点和文本的大小，并根据节点节点的频率调整边缘的权重。我已经为图创建了一个frequency属性，但这并不能解决向图传递有关节点节点频率的信息的问题。在

所以两部分的问题：
1） 将邻接矩阵转换为networkX图的最佳实践是什么？
2） 如何使用这些信息来缩放节点的大小和边的权重？在

## Compute Graph (G)
G = nx.Graph(A)

## Add frequency of word as attribute of graph
def Freq_Attribute(G, A):
    frequency = {}  # Dictionary Declaration
    for node in G.nodes():
        frequency[str(node)] = A[str(node)][str(node)]
    return nx.set_node_attributes(G, 'frequency', frequency)

Freq_Attribute(g,A) # Adds attribute frequency to graph, for font scale

## Plot Graph with Labels
plt.figure(1, figsize=(10,10))

# Set location of nodes as the default
pos = nx.spring_layout(G, k=0.50, iterations=30)  

# Nodes
node_size = 10000
nodes1 = nx.draw_networkx_nodes(G,pos,
                       node_color='None',
                       node_size=node_size,
                       alpha=1.0)  # nodelist=[0,1,2,3],
nodes1.set_edgecolor('#A9C1CD') # Set edge color to black

# Edges
edges = nx.draw_networkx_edges(G,pos,width=1,alpha=0.05,edge_color='black')
edges.set_zorder(3)

# Labels
nx.draw_networkx_labels(G,pos,labels=nx.get_node_attributes(G,'label'),
                        font_size=16, 
                        font_color='#062D40',
                        font_family='arial')  # sans-serif, Font=16
# node_labels = nx.get_node_attributes(g, 'name') 
# Use 'g.graph' to find attribute(s): {'name': 'words'}

plt.axis('off')
#plt.show()

我试过设置标签字体大小，但没用 字体大小=nx.get_node_属性（G，“频率”）+8）

Answer 1

为了满足您的需要，我尝试了以下方法：

import networkx as nx
import matplotlib.pyplot as plt

## create nx graph from adjacency matrix
def create_graph_from_adj(A):
    # A=[(n1, n2, freq),....]
    G = nx.Graph()
    for a in A:
        G.add_edge(a[0], a[1], freq=a[2])
    return G

A = [(0, 1, 0.5), (1, 2, 1.0), (2, 3, 0.8), (0, 2, 0.2), (3, 4, 0.1), (2, 4, 0.6)]
## Compute Graph (G)
G = create_graph_from_adj(A)

plt.subplot(121)

# Set location of nodes as the default
spring_pose = nx.spring_layout(G, k=0.50, iterations=30)  

nx.draw_networkx(G,pos=spring_pose)


plt.subplot(122)
# Nodes
default_node_size = 300
default_label_size = 12
node_size_by_freq = []
label_size_by_freq = []
for n in G.nodes():
    sum_freq_in = sum([G.edge[n][t]['freq'] for t in G.neighbors(n)])
    node_size_by_freq.append(sum_freq_in*default_node_size)
    label_size_by_freq.append(int(sum_freq_in*default_label_size))

nx.draw_networkx_nodes(G,pos=spring_pose,
                       node_color='red',
                       node_size=node_size_by_freq,
                       alpha=1.0)  
nx.draw_networkx_labels(G,pos=spring_pose,
                        font_size=12,  #label_size_by_freq is not allowed
                        font_color='#062D40',
                        font_family='arial') 

# Edges
default_width = 5.0
edge_width_by_freq = []
for e in G.edges():
    edge_width_by_freq.append(G.edge[e[0]][e[1]]['freq']*default_width)
nx.draw_networkx_edges(G,pos=spring_pose,
                       width=edge_width_by_freq,
                       alpha=1.0,
                       edge_color='black')

plt.show()

{a1}

首先，邻接反应不是用矩阵的形式给出的，但是这太单调了。在

其次，^{}不允许标签使用不同的字体大小。我帮不上忙。在

但是，最后一个边的大小和宽度允许。因此，它们分别根据其频率和输入频率的总和进行缩放。在

希望有帮助。在