24

使用 NetworkX 和新的库，用于社交网络分析查询。通过查询，我的意思是通过两个边节点的属性选择/创建子图，其中边创建路径，节点包含属性。该图使用形式为 MultiDiGraph

G2 = nx.MultiDiGraph()
G2.add_node( "UserA", { "type" :"Cat" } )
G2.add_node( "UserB", { "type" :"Dog" } )
G2.add_node( "UserC", { "type" :"Mouse" } )
G2.add_node( "Likes", { "type" :"Feeling" } )
G2.add_node( "Hates", { "type" :"Feeling" } )

G2.add_edge( "UserA", 'Hates' ,  statementid="1" )
G2.add_edge( "Hates", 'UserB' ,  statementid="1"  )
G2.add_edge( "UserC", 'Hates' ,  statementid="2" )
G2.add_edge( "Hates", 'UserA' ,  statementid="2"  )
G2.add_edge( "UserB", 'Hates' ,  statementid="3"  )
G2.add_edge( "Hates", 'UserA' ,  statementid="3"  )
G2.add_edge( "UserC", 'Likes' ,  statementid="3"  )
G2.add_edge( "Likes", 'UserB' ,  statementid="3"  )

查询

for node,data in G2.nodes_iter(data=True):
    if ( data['type'] == "Cat" ):
       # get all edges out from these nodes
            #then recursively follow using a filter for a specific statement_id

#or get all edges with a specific statement id
   # look for  with a node attribute of "cat"

有没有更好的查询方式？还是创建自定义迭代来创建子图的最佳实践？

或者（和一个单独的问题），可以简化图表，但我没有使用下图，因为“讨厌”类型的对象将有前身。这会使查询更简单吗？似乎更容易迭代节点

G3 = nx.MultiDiGraph()
G3.add_node( "UserA", { "type" :"Cat" } )
G3.add_node( "UserB", { "type" :"Dog" } )

G3.add_edge( "UserA", 'UserB' ,  statementid="1" , label="hates")
G3.add_edge( "UserA", 'UserB' ,  statementid="2" , label="hates")

其他注意事项：

  • 也许add_path为创建的路径添加一个标识符？
  • iGraph 有一个很好的查询功能 g.vs.select()
4

3 回答 3

26

编写一个单行代码来制作具有特定属性的节点列表或生成器非常简单（此处显示的生成器）

import networkx as nx

G = nx.Graph()
G.add_node(1, label='one')
G.add_node(2, label='fish')
G.add_node(3, label='two')
G.add_node(4, label='fish')

# method 1
fish = (n for n in G if G.node[n]['label']=='fish')
# method 2
fish2 = (n for n,d in G.nodes(data=True) if d['label']=='fish')

print(list(fish))
print(list(fish2))

G.add_edge(1,2,color='red')
G.add_edge(2,3,color='blue')

red = ((u,v) for u,v,d in G.edges(data=True) if d['color']=='red')

print(list(red))

如果您的图表很大且固定，并且您想要进行快速查找，您可以制作这样的属性的“反向字典”，

labels = {}
for n, d in G.nodes(data=True):
    l = d['label']
    labels[l] = labels.get(l, [])
    labels[l].append(n)
print labels
于 2013-03-27T04:14:36.427 回答
11

基于@Aric 的回答，你可以找到这样的红鱼：

red_fish = set(n for u,v,d in G.edges_iter(data=True)
               if d['color']=='red'
               for n in (u, v)
               if G.node[n]['label']=='fish')

print(red_fish)
# set([2])
于 2013-03-27T10:17:53.470 回答
9

为了根据边和节点的属性选择边，您可能需要使用图形 G2 执行以下操作：

def select(G2, query):
    '''Call the query for each edge, return list of matches'''
    result = []
    for u,v,d in G2.edges(data=True):
        if query(u,v,d):
            result.append([(u,v)])
    return result

# Example query functions
# Each assumes that it receives two nodes (u,v) and 
# the data (d) for an edge 

def dog_feeling(u, v, d):
    return (d['statementid'] == "3" 
            and G2.node[u]['type'] == "Dog"
            or G2.node[u]['type'] == "Dog")

def any_feeling(u,v,d):
    return (d['statementid'] == "3" 
            and G2.node[u]['type'] == "Feeling"
            or G2.node[u]['type'] == "Feeling")

def cat_feeling(u,v,d):
    return (G2.node[u]['type'] == "Cat"
            or G2.node[v]['type'] == "Cat")

# Using the queries
print select(G2, query = dog_feeling)
print select(G2, query = any_feeling)
print select(G2, query = cat_feeling)

这将迭代过程抽象到select()函数中，您可以将查询编写为单独的、可测试的函数。

于 2013-03-27T08:41:05.780 回答