社交网络的 Neo4j 属性百分比 [英] neo4j percentage of attribute for social network

问题描述

如何计算社交网络所有连接的属性百分比?在这个特定示例中，我想通过评估用户的交互(通话、短信)来计算用户的欺诈行为:

How can I calculate the percentage of an attribute for all the connections of a social network? In this particular sample I would want to calculate the fraudulence of a user by assessing its interactions (call, sms):

CREATE (Alice:Person {id:'a', fraud:1})
CREATE (Bob:Person {id:'b', fraud:0})
CREATE (Charlie:Person {id:'c', fraud:0})
CREATE (David:Person {id:'d', fraud:0})
CREATE (Esther:Person {id:'e', fraud:0})
CREATE (Fanny:Person {id:'f', fraud:0})
CREATE (Gabby:Person {id:'g', fraud:0})
CREATE (Fraudster:Person {id:'h', fraud:1})


CREATE
  (Alice)-[:CALL]->(Bob),
  (Bob)-[:SMS]->(Charlie),
  (Charlie)-[:SMS]->(Bob),
  (Fanny)-[:SMS]->(Charlie),
  (Esther)-[:SMS]->(Fanny),
  (Esther)-[:CALL]->(David),
  (David)-[:CALL]->(Alice),
  (David)-[:SMS]->(Esther),
  (Alice)-[:CALL]->(Esther),
  (Alice)-[:CALL]->(Fanny),
  (Fanny)-[:CALL]->(Fraudster)

当尝试查询时:

MATCH (a)-->(b)
WHERE b.fraud = 1
RETURN (count() / ( MATCH (a) -->(b) RETURN count() ) * 100)

我看到以下错误:

Invalid input '>': expected 0..9, '.', UnsignedHexInteger, UnsignedOctalInteger or UnsignedDecimalInteger (line 3, column 33 (offset: 66))
"RETURN (count() / ( MATCH (a) -->(b) RETURN count() ) * 100)"
                                 ^

推荐答案

此查询将返回每个欺诈的连接百分比:

This query will return the percentage of connections to each fraud:

MATCH (:Person)-[:CALL|:SMS]->(f:Person)
WITH TOFLOAT(COUNT(*))/100 AS divisor, COLLECT(f) AS fs
UNWIND fs AS f
WITH divisor, f
WHERE f.fraud = 1
RETURN f, COUNT(*)/divisor AS percentage

有了样本数据，结果是:

With the sample data, the result is:

+----------------------------------------------+
| f                        | percentage        |
+----------------------------------------------+
| Node[13]{id:"h",fraud:1} | 9.090909090909092 |
| Node[6]{id:"a",fraud:1}  | 9.090909090909092 |
+----------------------------------------------+

此查询只需要对数据库进行一次扫描，并且明确说明节点标签和关系类型——以过滤掉数据库中可能存在的任何其他数据.

This query only needs a single scan of the DB, and is explicit about the node labels and relationship types -- to filter out any other data that might be in the DB.

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