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))

我有2300个边缘与稀有连接.这是我的情节：

以下是它的一些部分的缩放：

此图不可读,因为边之间的距离太小.如何在边缘之间留出更大的距离？只有来自同一“家庭”的边缘距离很小.

有没有其他方法来改善有很多边缘的情节？
我正在寻找任何可视化父子关系的方法,它可能是另一个python包.

解决方法:

你似乎有很多小的,断开连接的组件.如果您想要一个信息图表,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单个巨型组件.因此,如果您需要合理的坐标,则通常需要分别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图表：

我已经使用networkx编写了此图的代码,因为这是我选择的模块.但是,用igraph函数替换networkx函数非常容易.您需要替换的两个函数是networkx.connected_component_subgraphs以及您要用于component_layout_func的任何函数.

#!/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()

编辑

如果你想紧密排列子图,你需要安装rectangle-packer(pip install rectangle-packer),并用这个版本替换_get_component_bboxes：

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]