我有以下脚本：
import pandas as pd
from igraph import *
df_p_c = pd.read_csv('data/edges.csv')
...
edges = list_edges
vertices = list(dict_case_to_number.keys())
g = Graph(edges=edges, directed=True)
plot(g, bbox=(6000, 6000))
I have 2300 edges with rare connection. This is my plot of it:
And here are zooms of a few parts of it:
该图不可读，因为边缘之间的距离太小。如何使边缘之间的距离更大？只有来自同一“族”的边距离较小。
还有其他方法可以改善具有大量边缘的绘图吗？ 我正在寻找任何可视化父子相关性的方法，它可能是另一个 python 数据包。
你似乎有很多小的、互不相连的组件。如果您想要一个信息丰富的图表，我认为您应该按大小对连接的组件进行排序和分组。此外，许多网络布局算法的基本假设是存在一个巨大的组件。因此，如果您想要合理的坐标，您通常需要单独计算每个组件的布局，然后相对于彼此排列组件。我会以这种方式重新绘制你的图表：
我已经使用该图编写了代码networkx因为这是我选择的模块。然而，用它来代替是很容易的networkx函数与igraph功能。您需要替换的两个函数是networkx.connected_component_subgraphs以及你想用的任何东西component_layout_func.
#!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
import networkx
def layout_many_components(graph,
component_layout_func=networkx.layout.spring_layout,
pad_x=1., pad_y=1.):
"""
Arguments:
----------
graph: networkx.Graph object
The graph to plot.
component_layout_func: function (default networkx.layout.spring_layout)
Function used to layout individual components.
You can parameterize the layout function by partially evaluating the
function first. For example:
from functools import partial
my_layout_func = partial(networkx.layout.spring_layout, k=10.)
pos = layout_many_components(graph, my_layout_func)
pad_x, pad_y: float
Padding between subgraphs in the x and y dimension.
Returns:
--------
pos : dict node : (float x, float y)
The layout of the graph.
"""
components = _get_components_sorted_by_size(graph)
component_sizes = [len(component) for component in components]
bboxes = _get_component_bboxes(component_sizes, pad_x, pad_y)
pos = dict()
for component, bbox in zip(components, bboxes):
component_pos = _layout_component(component, bbox, component_layout_func)
pos.update(component_pos)
return pos
def _get_components_sorted_by_size(g):
subgraphs = list(networkx.connected_component_subgraphs(g))
return sorted(subgraphs, key=len)
def _get_component_bboxes(component_sizes, pad_x=1., pad_y=1.):
bboxes = []
x, y = (0, 0)
current_n = 1
for n in component_sizes:
width, height = _get_bbox_dimensions(n, power=0.8)
if not n == current_n: # create a "new line"
x = 0 # reset x
y += height + pad_y # shift y up
current_n = n
bbox = x, y, width, height
bboxes.append(bbox)
x += width + pad_x # shift x down the line
return bboxes
def _get_bbox_dimensions(n, power=0.5):
# return (np.sqrt(n), np.sqrt(n))
return (n**power, n**power)
def _layout_component(component, bbox, component_layout_func):
pos = component_layout_func(component)
rescaled_pos = _rescale_layout(pos, bbox)
return rescaled_pos
def _rescale_layout(pos, bbox):
min_x, min_y = np.min([v for v in pos.values()], axis=0)
max_x, max_y = np.max([v for v in pos.values()], axis=0)
if not min_x == max_x:
delta_x = max_x - min_x
else: # graph probably only has a single node
delta_x = 1.
if not min_y == max_y:
delta_y = max_y - min_y
else: # graph probably only has a single node
delta_y = 1.
new_min_x, new_min_y, new_delta_x, new_delta_y = bbox
new_pos = dict()
for node, (x, y) in pos.items():
new_x = (x - min_x) / delta_x * new_delta_x + new_min_x
new_y = (y - min_y) / delta_y * new_delta_y + new_min_y
new_pos[node] = (new_x, new_y)
return new_pos
def test():
from itertools import combinations
g = networkx.Graph()
# add 100 unconnected nodes
g.add_nodes_from(range(100))
# add 50 2-node components
g.add_edges_from([(ii, ii+1) for ii in range(100, 200, 2)])
# add 33 3-node components
for ii in range(200, 300, 3):
g.add_edges_from([(ii, ii+1), (ii, ii+2), (ii+1, ii+2)])
# add a couple of larger components
n = 300
for ii in np.random.randint(4, 30, size=10):
g.add_edges_from(combinations(range(n, n+ii), 2))
n += ii
pos = layout_many_components(g, component_layout_func=networkx.layout.circular_layout)
networkx.draw(g, pos, node_size=100)
plt.show()
if __name__ == '__main__':
test()
如果你想让子图紧密排列，你需要安装矩形打包器（pip install rectangle-packer），并代入_get_component_bboxes有了这个版本：
import rpack
def _get_component_bboxes(component_sizes, pad_x=1., pad_y=1.):
dimensions = [_get_bbox_dimensions(n, power=0.8) for n in component_sizes]
# rpack only works on integers; sizes should be in descending order
dimensions = [(int(width + pad_x), int(height + pad_y)) for (width, height) in dimensions[::-1]]
origins = rpack.pack(dimensions)
bboxes = [(x, y, width-pad_x, height-pad_y) for (x,y), (width, height) in zip(origins, dimensions)]
return bboxes[::-1]
我写了一个用于可视化网络的库，它被称为netgraph https://github.com/paulbrodersen/netgraph。它以上述方式自动处理具有多个组件的网络。它与 networkx 和 igraph Graph 对象完全兼容，因此应该可以轻松快速地制作漂亮的图的图（至少是这样的想法）。
import itertools
import matplotlib.pyplot as plt
import networkx as nx
# installation easiest via pip:
# pip install netgraph
from netgraph import Graph
# construct the graph as before:
g = nx.Graph()
# add 30 unconnected nodes
g.add_nodes_from(range(30))
# add 15 2-node components
g.add_edges_from([(ii, ii+1) for ii in range(30, 60, 2)])
# add 10 3-node components
for ii in range(60, 90, 3):
g.add_edges_from([(ii, ii+1), (ii, ii+2), (ii+1, ii+2)])
# add a couple of larger components
n = 90
for ii in [10, 20, 40]:
g.add_edges_from(itertools.combinations(range(n, n+ii), 2))
n += ii
# if there are any disconnected components, netgraph automatically handles them separately
Graph(g, node_layout='circular', node_size=1, node_edge_width=0.1, edge_width=0.1, edge_color='black', edge_alpha=1.)
plt.show()