如何使用NetworkX绘制社区图

Question

如何使用NetworkX绘制社区图

pythongraphnetworkx

28

我如何使用Python Networkx绘制像这个图片中的社区一样的图表：

图片链接

- fullOfQuestion

1个回答

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- Paul Brodersen · Accepted Answer

networkx.draw_networkx_nodes和networkx.draw_networkx_edges的文档说明了如何设置节点和边的颜色。创建社区的边界补丁可以通过找到每个社区的节点位置，然后绘制一个包含所有位置（以及更多位置）的补丁（例如matplotlib.patches.Circle）来实现。

困难的部分是图形布局/设置节点位置。据我所知，在networkx中没有一种例行程序能够“开箱即用”地实现期望的图形布局。你想要做的是以下几点：

将社区与彼此相关联的位置：创建一个新的加权图，其中每个节点对应于一个社区，权值对应于社区之间的边数。使用你喜欢的图形布局算法（例如spring_layout）得到一个合理的布局。
在每个社区内定位节点：为每个社区创建一个新的图形。找到子图的位置布局。
结合1）和3）中的节点位置。例如，通过因子10缩放在1）中计算出的社区位置；将这些值添加到该社区内所有节点（如在2）中计算的）的位置。

~~我一直想实现这个功能。我可能会在今天晚些时候或周末做这件事。~~

编辑：

完成了。现在你只需要在节点的周围（后面）绘制你喜爱的图形补丁即可。

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

补充

虽然这个想法本身是可行的，但我之前的实现存在一些问题。最重要的是，该实现对于大小不均的社区效果不佳。具体来说，_position_communities 在画布上为每个社区分配相同的空间。如果某些社区比其他社区大得多，这些社区就会被压缩到与小社区相同的空间中。显然，这并不很好地反映了图的结构。

我编写了一个名为NetGraph的网络可视化库。它包含了一个改进后的社区布局程序，考虑到社区的大小进行排列。它完全兼容networkx和igraph图对象，因此制作出漂亮的图形应该轻松快捷（至少这是我们的设想）。

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()