与networkx

Question

如何绘制社区怎样绘制的图形与它的使用python networkx这样的形象社区：

image url

Answer 1

的文档networkx.draw_networkx_nodes和networkx.draw_networkx_edges介绍如何设置节点和边缘颜色。可以通过找到每个社区的节点位置，然后绘制包含所有位置（然后是一些）的补丁（例如matplotlib.patches.Circle）来制作围绕社区的补丁。

硬性位是图形布局/设置节点位置。 AFAIK，networkx中没有例行程序来实现所需的图形布局“开箱即用”。你想要做的是以下几点：

1）定位社区之间的相互关系：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。用您最喜欢的图形布局算法获得体面的布局（例如spring_layout）。

2）定位每个社区中的节点：为每个社区创建一个新图。找到子图的布局。

3）合并1）和3）中的节点位置。例如。以1）计算的大规模社区职位的10倍;将这些值添加到该社区中所有节点的位置（如2中计算））。

我一直想实现这一段时间。我可能会在今天晚些时候或周末做。

编辑：

瞧。现在你只需要在节点周围（后面）绘制你最喜欢的补丁。

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=partition.values()); plt.show() 
    return