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Spark graphFrames 文档有一个很好的示例如何应用聚合消息功能。

对我来说，它似乎只计算单个顶点和第一个顶点的朋友/连接，而不是作为 graphXs pregel 运算符更深入地迭代到图中。

我如何在graphFrames中完成这样的迭代，以及使用类似于这里如何处理迭代的聚合消息https://github.com/sparkling-graph/sparkling-graph/blob/master/operators/src/main/scala/ml/ graphX 中的sparkling/graph/operators/measures/vertex/eigenvector/ EigenvectorCentrality.scala？

import org.graphframes.examples
import org.graphframes.lib.AggregateMessages
val g: GraphFrame = examples.Graphs.friends  // get example graph

// We will use AggregateMessages utilities later, so name it "AM" for short.
val AM = AggregateMessages

// For each user, sum the ages of the adjacent users.
val msgToSrc = AM.dst("age")
val msgToDst = AM.src("age")
val agg = g.aggregateMessages
  .sendToSrc(msgToSrc)  // send destination user's age to source
  .sendToDst(msgToDst)  // send source user's age to destination
  .agg(sum(AM.msg).as("summedAges"))  // sum up ages, stored in AM.msg column
agg.show()

http://graphframes.github.io/user-guide.html#message-passing-via-aggregatemessages

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