首先，我是NEO4J的新手。现在我想从NEO4J获得这样的数据格式，以便与neo4jd3.js一起使用
我的查询如下
MATCH graph = (n:Node)-[r:CITE]-(m:Node)
where n.cluster = "79835"
return {
data: [{
graph:
{
nodes:collect({
id: n.id,
labels: ["Node"],
properties:
{
cluster: n.cluster
}
}),
nodes:collect({
id: m.id,
labels: ["Node"],
properties:
{
cluster: m.cluster
}
}),
relationships:collect({
startNode: n.id,
endNode: m.id,
id: toString(id(r)),
type: "CITE",
properties:
{
useclass: r.usage_class,
context: r.context,
weight: r.weight
}
})
}
}]
}
这段代码将给出这样的结果
[
{
"keys": [
"{ \n\t\tdata: [{\n graph: \n {\n\t\t\t\tnodes:collect({\n id: n.id,\n labels: [\"Node\"],\n properties:\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\tcluster: n.cluster\n\t\t\t\t\t\t\t\t}\n }),\n nodes:collect({\n id: m.id,\n labels: [\"Node\"],\n properties:\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\tcluster: m.cluster\n }\n }),\n relationships:collect({\n startNode: n.id,\n endNode: m.id,\n id: toString(id(r)),\n type: \"CITE\",\n properties:\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\tuseclass: r.usage_class,\n context: r.context,\n weight: r.weight\n }\n })\n\t\t\t}\n\t\t}]\n}"
],
"length": 1,
"_fields": [
{
"data": [
{
"graph": {
"nodes": [
{
"id": "1188",
"labels": [
"Node"
],
"properties": {
"cluster": "245591"
}
},
{
"id": "22356",
"labels": [
"Node"
],
"properties": {
"cluster": "24831"
}
},
{
"id": "9717",
"labels": [
"Node"
],
"properties": {
"cluster": "10774"
}
}
],
"relationships": [
{
"endNode": "1188",
"id": "11779",
"startNode": "22350",
"properties": {
"useclass": "MENTION",
"context": "- e biological research. Although it is possible to obtain biologically meaningful results with these algorithms, some of their characteristics often complicate their use for clustering expression data =-=[43]-=-. They require, for example, the predefinition of one or more user-defined parameters that are hard to estimate by a biologist (e.g., the predefinition of the number of clusters in -means and SOM\n",
"weight": "1"
},
"type": "CITE"
},
{
"endNode": "22356",
"id": "11778",
"startNode": "22350",
"properties": {
"useclass": "EXTEND",
"context": "- periment. In a two-channel cDNA microarray experiment, for example, normalization adjusts for differences in labeling, detection efficiency, and in the quantity of initial RNA within the two channels =-=[23]-=-. Normalization is necessary before one can compare the results from different microarray experiments. Second, transformation of the data using a nonlinear function (often the logarithm is used, espec\n",
"weight": "1"
},
"type": "CITE"
}
]
}
}
]
}
],
"_fieldLookup": {
"{ \n\t\tdata: [{\n graph: \n {\n\t\t\t\tnodes:collect({\n id: n.id,\n labels: [\"Node\"],\n properties:\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\tcluster: n.cluster\n\t\t\t\t\t\t\t\t}\n }),\n nodes:collect({\n id: m.id,\n labels: [\"Node\"],\n properties:\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\tcluster: m.cluster\n }\n }),\n relationships:collect({\n startNode: n.id,\n endNode: m.id,\n id: toString(id(r)),\n type: \"CITE\",\n properties:\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\tuseclass: r.usage_class,\n context: r.context,\n weight: r.weight\n }\n })\n\t\t\t}\n\t\t}]\n}": 0
}
} ]
我的问题是仅返回了m个节点。我希望此密码以这种格式返回结果。结果应同时包含n个节点和m个节点。
感谢您的帮助。