Python 如何使用networkx绘制社区

如何使用python networkx绘制社区图，如下图所示：

---

有关

networkx.draw\u networkx\u节点和
networkx.draw\u networkx\u边的文档说明了如何设置节点和边的颜色。可以通过查找每个社区的节点位置，然后绘制包含所有位置（以及部分位置）的面片（例如，
matplotlib.patches.Circle
）来创建社区边界面片

难点在于图形布局/设置节点位置。
顺便说一句，networkx中没有实现“开箱即用”所需图形布局的例程。您希望执行以下操作：

相对于彼此定位社区：创建一个新的加权图，其中每个节点对应一个社区，权重对应于社区之间的边数。使用您喜爱的图形布局算法（例如，
spring\u布局
）获得一个像样的布局


在每个社区中定位节点：为每个社区创建一个新的图形。查找子图的布局


组合1）和3）中的节点位置。例如，按1）中的系数10计算社区职位；将这些值添加到该社区中所有节点的位置（如2中计算的）


我一直想实现这一点有一段时间了。我可能今天晚些时候或者周末做

编辑：
瞧。现在，您只需要在节点周围（后面）绘制您最喜欢的补丁


补遗
虽然总体思路是合理的，但我上面的旧实现存在一些问题。最重要的是，对于规模不均的社区，实施效果并不理想。具体来说，
\u position\u communities
在画布上为每个社区提供相同数量的不动产。如果一些社区比其他社区大得多，那么这些社区最终会被压缩到与小社区相同的空间中。显然，这并不能很好地反映图形的结构

我写了一个可视化网络的库，叫做。它包括上述社区布局例程的改进版本，在安排社区时还考虑了社区的大小。它与
networkx
和
igraph
图形对象完全兼容，因此制作美观的图形应该简单快捷（至少是这样）


哇！这是个好主意。感谢您的实施倒数第二行需要是
nx.draw（g，pos，node_color=list（partition.values（））
@mortezashahrarinia谢谢您的提醒。显然，他们改变了
分区的类型。现在变了。
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx

def community_layout(g, partition):
    """
    Compute the layout for a modular graph.


    Arguments:
    ----------
    g -- networkx.Graph or networkx.DiGraph instance
        graph to plot

    partition -- dict mapping int node -> int community
        graph partitions


    Returns:
    --------
    pos -- dict mapping int node -> (float x, float y)
        node positions

    """

    pos_communities = _position_communities(g, partition, scale=3.)

    pos_nodes = _position_nodes(g, partition, scale=1.)

    # combine positions
    pos = dict()
    for node in g.nodes():
        pos[node] = pos_communities[node] + pos_nodes[node]

    return pos

def _position_communities(g, partition, **kwargs):

    # create a weighted graph, in which each node corresponds to a community,
    # and each edge weight to the number of edges between communities
    between_community_edges = _find_between_community_edges(g, partition)

    communities = set(partition.values())
    hypergraph = nx.DiGraph()
    hypergraph.add_nodes_from(communities)
    for (ci, cj), edges in between_community_edges.items():
        hypergraph.add_edge(ci, cj, weight=len(edges))

    # find layout for communities
    pos_communities = nx.spring_layout(hypergraph, **kwargs)

    # set node positions to position of community
    pos = dict()
    for node, community in partition.items():
        pos[node] = pos_communities[community]

    return pos

def _find_between_community_edges(g, partition):

    edges = dict()

    for (ni, nj) in g.edges():
        ci = partition[ni]
        cj = partition[nj]

        if ci != cj:
            try:
                edges[(ci, cj)] += [(ni, nj)]
            except KeyError:
                edges[(ci, cj)] = [(ni, nj)]

    return edges

def _position_nodes(g, partition, **kwargs):
    """
    Positions nodes within communities.
    """

    communities = dict()
    for node, community in partition.items():
        try:
            communities[community] += [node]
        except KeyError:
            communities[community] = [node]

    pos = dict()
    for ci, nodes in communities.items():
        subgraph = g.subgraph(nodes)
        pos_subgraph = nx.spring_layout(subgraph, **kwargs)
        pos.update(pos_subgraph)

    return pos

def test():
    # to install networkx 2.0 compatible version of python-louvain use:
    # pip install -U git+https://github.com/taynaud/python-louvain.git@networkx2
    from community import community_louvain

    g = nx.karate_club_graph()
    partition = community_louvain.best_partition(g)
    pos = community_layout(g, partition)

    nx.draw(g, pos, node_color=list(partition.values())); plt.show()
    return

import matplotlib.pyplot as plt
import networkx as nx

# installation easiest via pip:
# pip install netgraph
from netgraph import Graph

# create a modular graph
partition_sizes = [10, 20, 30, 40]
g = nx.random_partition_graph(partition_sizes, 0.5, 0.1)

# since we created the graph, we know the best partition:
node_to_community = dict()
node = 0
for community_id, size in enumerate(partition_sizes):
    for _ in range(size):
        node_to_community[node] = community_id
        node += 1

# # alternatively, we can infer the best partition using Louvain:
# from community import community_louvain
# node_to_community = community_louvain.best_partition(g)

community_to_color = {
    0 : 'tab:blue',
    1 : 'tab:orange',
    2 : 'tab:green',
    3 : 'tab:red',
}
node_color = {node: community_to_color[community_id] for node, community_id in node_to_community.items()}

Graph(g,
      node_color=node_color, node_edge_width=0, edge_alpha=0.1,
      node_layout='community', node_layout_kwargs=dict(node_to_community=node_to_community),
      edge_layout='bundled', edge_layout_kwargs=dict(k=2000),
)

plt.show()