如何在 python networkX 包中显示二分图，左侧列中一个类的节点，右侧列中另一类的节点？
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 位于左侧的列中，节点 'a'、'b'、'c' 位于右侧的列中，边缘位于它们之间。
【问题讨论】：
每个节点的位置需要自己设置：
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()
【讨论】：
with_labels=True 添加到nx.draw(B, pos=pos)
基于@Rikka 的回答和较新版本的 NetworkX，以下自动（并改进）了二分网络的定位。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()
【讨论】：
根据上面的@Rikka 回答我自己的问题——这里是确定任意多部分图中节点位置的代码，给定的部分名称。
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，假设我定义了一个像这样的三方图（这个例子中权重无关）：
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！
【讨论】：