how can I visual a dense graph obviously in networkx python package?

Question

I have a large, dense directed graph in python, made with the NetworkX package. How can I improve the clarity of the graph image?

The following image shows my graph.

Answer 1

I can recommend you several ways to improve your graph visualization depending on its size.

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If you want to visualize a large graph (>1000 nodes), you can read some tricks in my another answer. In your case I recommend you to import the graph to a large vector picture:

import networkx as nx 
import matplotlib.pyplot as plt 
fig = plt.figure(figsize=(40, 40)) 
G = nx.fast_gnp_random_graph(300, 0.02, seed=1337) 
nx.draw(G, node_size=30) 
plt.axis('equal') 
plt.show() 
fig.savefig('waka.svg') 

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If you have relatively small graph (<1000 nodes), you can play with graph layouts.

The most suitable layout for your kind of graph is the default spring_layout. It has k argument that set the optimal distance between nodes. Here is the example:

Default k value

import networkx as nx
import random
random.seed(1234)
G = nx.fast_gnp_random_graph(30, 0.4, seed=1337)
for i in range(20):
    G.add_edge(i + 40, random.randint(1, 30))
    G.add_edge(i + 40, random.randint(1, 30))
pos = nx.spring_layout(G, seed=4321)
nx.draw(G, pos=pos, node_size=30, node_color='red')

Enlarged k value

import networkx as nx
import random
random.seed(1234)
G = nx.fast_gnp_random_graph(30, 0.4, seed=1337)
for i in range(20):
    G.add_edge(i + 40, random.randint(1, 30))
    G.add_edge(i + 40, random.randint(1, 30))
pos = nx.spring_layout(G, seed=4321, k=2)
nx.draw(G, pos=pos, node_size=30, node_color='red')

It is less readable if you need analyse edges with high precision, but it is better if you are care more about nodes.