您可以查看Memgraph、高性能、内存和事务图形数据库。openCypher和Bolt兼容。(免责声明：我是联合创始人兼首席技术官)。Memgraph内置了加权最短路径功能，其中总权重通过用户定义的lambda函数计算。基于此数据集

CREATE (n1 {id: 1}) CREATE (n2 {id: 2}) CREATE (n3 {id: 3}) CREATE (n4 {id: 4})
CREATE (n1)-[:E {weight1: 1, weight2: 1}]->(n2)
CREATE (n1)-[:E {weight1: 2, weight2: 10}]->(n3)
CREATE (n2)-[:E {weight1: 3, weight2: 100}]->(n4)
CREATE (n3)-[:E {weight1: 4, weight2: 1}]->(n4);

相关Memgraph的查询是

MATCH (a {id: 1})-[
          edges *wShortest (e, n | e.weight1 + e.weight2) total_weight
      ]-(b {id: 4})
RETURN startNode(head(edges)).id +
       reduce(acc = "", edge IN edges | acc + " -> " + endNode(edge).id) AS hops,
       total_weight;

结果如下。

| hops      | total_weight |
|-----------|--------------|
|1 -> 3 -> 4| 17.0         |

对于apoC，您需要使用一个额外的属性来保存总和。

对于图形算法中的一个，您可以使用将两个权重相加的图形投影。

Https://neo4j-contrib.github.io/neo4j-graph-algorithms/#_single_source_shortest_path

您可以在配置节中使用nodeQuery和relationshipQuery。

CALL algo.shortestPath.stream(start, end, 'weight',
{nodeQuery:'match (n) return id(n) as id', 
 relationshipQuery:'match (n)-[r]->(m) return id(n) as source, id(m) as target, r.weight1+r.weight2 as weight', 
defaultValue:1.0,direction:'OUTGOING'}) 
YIELD nodeId, cost