如何使用 python networkX 包显示二分图？

Question

如何在 python networkX 包中显示二分图，其中一个类的节点在左侧的列中，而另一个类的节点在右侧？

我可以创建一个图表并像这样显示它

B = nx.Graph()
B.add_nodes_from([1,2,3,4], bipartite=0) # Add the node attribute "bipartite"
B.add_nodes_from(['a','b','c'], bipartite=1)
B.add_edges_from([(1,'a'), (1,'b'), (2,'b'), (2,'c'), (3,'c'), (4,'a')])
nx.draw(B)
plt.show()

但我希望节点 1、2、3、4 在一列的左侧，节点“a”、“b”、“c”在右侧的一列，它们之间有边。

Answer 1

需要自己设置每个节点的位置：

B = nx.Graph()
B.add_nodes_from([1,2,3,4], bipartite=0) # Add the node attribute "bipartite"
B.add_nodes_from(['a','b','c'], bipartite=1)
B.add_edges_from([(1,'a'), (1,'b'), (2,'b'), (2,'c'), (3,'c'), (4,'a')])

# Separate by group
l, r = nx.bipartite.sets(B)
pos = {}

# Update position for node from each group
pos.update((node, (1, index)) for index, node in enumerate(l))
pos.update((node, (2, index)) for index, node in enumerate(r))

nx.draw(B, pos=pos)
plt.show()

Answer 2

基于@Rikka 的回答和更新版本的 NetworkX，以下自动化（并改进）二分网络的定位。我还为网络的不同分区添加了标签和不同的颜色。

B = networkx.Graph()
B.add_nodes_from([1,2,3,4], bipartite=0) # Add the node attribute "bipartite"
B.add_nodes_from(['abc','bcd','cef'], bipartite=1)
B.add_edges_from([(1,'abc'), (1,'bcd'), (2,'bcd'), (2,'cef'), (3,'cef'), (4,'abc')])

top = networkx.bipartite.sets(B)[0]
pos = networkx.bipartite_layout(B, top)
networkx.draw(B, pos=pos, with_labels=True, node_color=['green','green','green','green','blue','blue','blue'])
plt.show()

Answer 3

根据上面的@Rikka 回答我自己的问题——这是确定任意多部分图中节点位置的代码，给定部分的名称。

import networkx as nx
def position_MultiPartiteGraph( Graph, Parts ):
    # Graph is a networkX Graph object, where the nodes have attribute 'agentType' with part name as a value
    # Parts is a list of names for the parts (to be shown as columns)
    # returns list of dictionaries with keys being networkX Nodes, values being x,y coordinates for plottingxPos = {}
    xPos = {}
    yPos = {}
    for index1, agentType in enumerate(Parts):
        xPos[agentType] = index1
        yPos[agentType] = 0

    pos = {}
    for node, attrDict in Graph.nodes(data=True):
        agentType = attrDict['agentType']
        # print ('node: %s\tagentType: %s' % (node, agentType))
        # print ('\t(x,y): (%d,%d)' % (xPos[agentType], yPos[agentType]))
        pos[node] = (xPos[agentType], yPos[agentType])
        yPos[agentType] += 1

    return pos

现在，假设我定义了一个这样的三方图（权重与这个例子无关）：

TG = nx.Graph()
TG.add_nodes_from([1,2,3,4], agentType='world') # Add the node attribute   "bipartite"
TG.add_nodes_from(['a','b','c'], agentType='sender')
TG.add_nodes_from(['A','B','C'], agentType='receiver')

# This is just an easier way to add (and to automatically generate) weighted edges
myEdges = [(1,'a',0.75),
       (1,'b',0.25),
       (2,'b',0.5),
       (2,'c',0.5),
       (3,'c',1.0),
       (4,'a',1.0),
       ('a','C',0.10),
       ('a','A',0.80),
       ('c','A',1.0),
       ('b','C',1.0)]

[TG.add_edge(x,y,weight=z) for x,y, z in myEdges]

那么这里是如何使用它：

nx.draw(TG,pos=position_MultiPartiteGraph(TG, ['world', 'sender', 'receiver']))
plt.show()

我不确定如何显示输出，但它对我有用！欢呼！谢谢@Rikka！