图之间的结构运算符

Question

这个问题是对上一个问题的 "sequel"。我是 spark graphx 和 scala 的新手，我想知道如何执行下面的操作。

如何将两个图合并为一个新图，使新图具有以下内容属性：

The attributes of the common edges of the two graphs are averaged (or in a more general way, apply an averaging function between the edge attributes (edge attributes are of type double))

我们认为公共边=相同的srcId和相同的dstId，并且顶点和边是唯一的。

Answer 1

假设您只有两个图并且都包含相同的一组顶点而没有重复的边，您可以使用组合边并在新图上使用 groupEdges 方法：

val graph1: Graph[T,Double] = ???
val graph2: Graph[T,Double] = ???

Graph(graph1.vertices, graph1.edges.union(graph2.edges))
  .groupEdges((val1, val2) => (val1 + val2) / 2.0)

或更通用一点：

Graph(graph1.vertices, graph1.edges.union(graph2.edges))
  .mapEdges(e => (e.attr, 1.0))
  .groupEdges((val1, val2) => (val1._1 + val2._1, val1._2 + val2._2))
  .mapEdges(e => e.attr._1 / e.attr._2)

如果这还不够，您可以组合值并从头开始创建新图表：

def edgeToPair (e: Edge[Double]) = ((e.srcId, e.dstId), e.attr)
val pairs1 = graph1.edges.map(edgeToPair)
val pairs2 = graph2.edges.map(edgeToPair)

// Combine edges
val newEdges = pairs1.union(pairs2)
  .aggregateByKey((0.0, 0.0))(
    (acc, e) => (acc._1 + e, acc._2 + 1.0),
    (acc1, acc2) => (acc1._1 + acc2._1, acc1._2 + acc2._2)
  ).map{case ((srcId, dstId), (acc, count)) => Edge(srcId, dstId, acc / count)}

// Combine vertices assuming there are no conflicts
// like different labels
val newVertices = graph1.vertices.union(graph2.vertices).distinct

// Create new graph
val newGraph = Graph(newVertices, newEdges)

其中 aggregateByKey 可以替换为 groupByKey，然后是需要所有值的映射，例如中位数。

图之间的结构运算符

Structural Operators between graphs

scala

apache-spark

spark-graphx