在NetworkX中按边和节点属性查询图的最佳实践
在NetworkX中按边和节点属性查询图的最佳实践
提问于 2013-03-27 02:26:53
使用NetworkX和新手库，用于社交网络分析查询。所谓查询，我是指通过边创建路径的两边节点的属性来选择/创建子图，并且节点包含属性。图形正在使用以下形式的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" )查询方式：
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？或者创建自定义迭代来创建子图是最佳实践吗？
或者(还有一个单独的问题)，Graph可以简化，但我不使用下图，因为"hates“类型的对象将具有predcessors。这会使查询变得更简单吗？似乎更容易遍历节点
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")其他注释：
- 也许
add_path会将标识符添加到创建的路径中？ - iGraph具有nice query feature path
回答 3
Stack Overflow用户
回答已采纳
发布于 2013-03-27 18:17:53
在@Aric's answer的基础上，你可以找到像这样的红鱼：
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])Stack Overflow用户
发布于 2013-03-27 12:14:36
编写一行代码来创建具有特定属性的节点列表或生成器非常简单(生成器在这里显示)
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))如果你的图很大很固定，你想要快速查找，你可以制作一个属性的“反向字典”，如下所示，
labels = {}
for n, d in G.nodes(data=True):
l = d['label']
labels[l] = labels.get(l, [])
labels[l].append(n)
print labelsStack Overflow用户
发布于 2013-03-27 16:41:06
为了根据边和节点的属性选择边，您可能需要使用图形G2执行类似以下操作：
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。
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/15644684
复制相关文章
相似问题