根据SPARQL查询和Pagerank值在draw_networkx可视化中突出显示节点

如何解决根据SPARQL查询和Pagerank值在draw_networkx可视化中突出显示节点

根据我上次提出的问题：Applying PageRank to a topic hierarchy tree(using SPARQL query extracted from DBpedia)

由于我目前相对于Regulated concept map获得了PageRank值。对于“ Machine_learning”的概念，我当前的代码如下：

from SPARQLWrapper import SPARQLWrapper,N3
from rdflib import Graph,URIRef,Literal
import networkx as nx
from networkx.readwrite import json_graph
from rdflib.extras.external_graph_libs import rdflib_to_networkx_graph
from rdflib.namespace import Namespace,RDFS,FOAF
import matplotlib.pyplot as plt

#SPARQL query for Regulated SPARQL Query Strategy 
sparql = SPARQLWrapper("http://dbpedia.org/sparql")
sparql.setQuery("""construct { ?child skos:broader <http://dbpedia.org/resource/Category:Machine_learning> . ?gchild skos:broader ?child } 
where  { 

{ ?child skos:broader <http://dbpedia.org/resource/Category:Machine_learning> . ?gchild skos:broader ?child}  
UNION
{ ?gchild skos:broader/skos:broader <http://dbpedia.org/resource/Category:Machine_learning> . ?gchild skos:broader ?child}
}
""")


sparql.setReturnFormat(N3)
results = sparql.query().convert()
g = Graph()
g.parse(data=results,format="n3")
#Undirected graphs will be converted to a directed graph with two directed edges for each undirected edge.
dg = rdflib_to_networkx_graph(g,False,edge_attrs=lambda s,p,o:{})

#Draw regulated concept map
nx.draw(dg)
plt.draw()

#PageRank calculation
p1 = nx.pagerank(dg,alpha=0.85)

#p1 to pr(dict to list)
pr = sorted(p1.items(),key=lambda x:x[1],reverse=True)[:10]

#print sorted ranking
for key,val in pr:
    print(key,val)

有几个问题：

1. 如何根据SPARQL查询在draw_networkx可视化中突出显示节点？例如，我想将此查询中的节点{ ?child skos:broader <http://dbpedia.org/resource/Category:Machine_learning> . ?gchild skos:broader ?child} 分配为绿色，{ ?gchild skos:broader/skos:broader <http://dbpedia.org/resource/Category:Machine_learning> . ?gchild skos:broader ?child}分配为红色。
2. 我是否可以根据上面计算的PageRank值来调整节点大小并为这些节点分配另一种颜色？

#Draw regulated concept map
# nx.draw(dg,pos=nx.spring_layout(dg),node_color='red') # use spring layout
# edges = nx.draw_networkx_edges(dg,pos=nx.spring_layout(dg))

pos = nx.spring_layout(dg)
source_node=copy.copy(pos)
print(source_node)
source_node_list = list(source_node.keys())
# print(source_node_list[0] in nx.spring_layout(dg))
# print(source_node_list)


options = {"node_size": 25,"alpha": 0.85}
graph=nx.draw_networkx_edges(dg,pos=pos,width=1.0,alpha=0.5)
graph=nx.draw_networkx_nodes(dg,nodelist=[source_node_list[1]],node_color="r",**options)
graph=nx.draw_networkx_nodes(dg,nodelist=[source_node_list[0],],node_color="b",nodelist=source_node_list[2:len(source_node_list)-1],node_color="g",**options)

# nx.draw(graph)
# plt.draw()


# nx.draw_networkx_edges(
#     dg,#     pos,#     edgelist=[source_node_list[1]],#     width=8,#     alpha=0.5,#     edge_color="r",# )
# nx.draw_networkx_edges(
#     dg,#     edgelist=[source_node_list[0]],#     edge_color="b"
# )

# nx.draw_networkx(dg,node_color='blue',with_labels = False)
# labels=nx.draw_networkx_labels(dg,pos=nx.spring_layout(dg))
# nodes = nx.draw_networkx_nodes(dg,pos=nx.spring_layout(dg))

# nx.draw(dg)
# plt.draw()

非常感谢您。

解决方法

我认为您可以将字典传递给draw函数的node_color参数。如果构造该词典，使键是节点名，值是要与这些节点名关联的颜色，那么您应该能够获得所需的格式。

例如如果您已经能够运行一些SPARQL来生成想要为绿色的节点列表，以及想要为蓝色的另一个列表，并假设您拥有green_list和blue_list对这些节点名称的列表，那么您可以构建这样的字典：

# create the colour specific dictionaries
blue_dict = { n : "blue" for n in blue_list }
green_dict = { n : "green" for n in green_list }
# merge them together into a combined dictionary
known_colour_d = { **blue_dict,**green_dict }
# construct the final dictionary,leaving unknown values with a colour of "orange"
node_colours_d = { n : known_colour_d.get(n,"orange") for n in dg.nodes() }

理想情况下，然后在绘制时将node_colours_d放入参数中，它将为您着色。从内存来看，某些nx版本更喜欢颜色为delivered as a list，该颜色与节点名称具有相同的顺序-但是我认为这对于当前版本应该有效。

或者，假设nx需要一个列表，则可以通过替换以下内容，将node_colours_d替换为node_colours_l以执行相同的工作：

node_colours_l = [ known_colour_d.get(n,"orange") for n in dg.nodes() ]

这将创建一个列表，其中包含按顺序映射到图形中每个节点的外观的颜色，并将此列表提交到绘图函数的node_color参数。