Question

假设有一个简单的图形，如：

val users = sc.parallelize(Array(
                 (1L, Seq("M", 2014, 40376, null, "N", 1, "Rajastan")),
                 (2L, Seq("M", 2009, 20231, null, "N", 1, "Rajastan")),
                 (3L, Seq("F", 2016, 40376, null, "N", 1, "Rajastan"))
            ))                                
val edges = sc.parallelize(Array(
                 Edge(1L, 2L, ""), 
                 Edge(1L, 3L, ""), 
                 Edge(2L, 3L, "")))
val graph = Graph(users, edges)

我想计算每个顶点与每个属性上的邻居相似的程度。

理想输出（RDD或DataFrame）可以保存这些结果：

1L: 0.5, 0.5, 0.5, 1.0, 1.0, 1.0, 1.0
2L: 0.5, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0
3L: 0.0, 0.5, 0.5, 1.0, 1.0, 1.0, 1.0

例如，1L的第一个值意味着在2个邻居上，只有1个共享相同的值......

我正在使用aggregateMessage来计算有多少邻居具有相似的属性值但到目前为止无济于事：

val result = graph.aggregateMessages[(Int, Seq[Any])](
    // build the message
    sendMsg = {
        // map function
        triplet =>
        // send message to destination vertex
        triplet.sendToDst(1, triplet.srcAttr)
        // send message to source vertex 
        triplet.sendToSrc(1, triplet.dstAttr)
    }, // trying to count neighbors with similar property
    { case ((cnt1, sender), (cnt2, receiver)) =>
        val prop1 = if(sender(0) == receiver(0)) 1d else 0d
        val prop2 = if(Math.abs(sender(1).asInstanceOf[Int] - receiver(1).asInstanceOf[Int])<3) 1d else 0d
        val prop3 = if(sender(2) == receiver(2)) 1d else 0d
        val prop4 = if(sender(3) == receiver(3)) 1d else 0d
        val prop5 = if(sender(4) == receiver(4)) 1d else 0d
        val prop6 = if(sender(5) == receiver(5)) 1d else 0d
        val prop7 = if(sender(6) == receiver(6)) 1d else 0d
        (cnt1 + cnt2, Seq(prop1, prop2, prop3, prop4, prop5, prop6, prop7))
    }
)

这给了我每个顶点的正确邻域大小，但没有正确地总结这些值：

//> (1,(2,List(0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0)))
//| (2,(2,List(0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0)))
//| (3,(2,List(1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0)))

Answer 1

它不会对值进行求和，因为代码中没有总和。而且你的逻辑错了。 mergeMsg会收到不是（message，current）对的邮件。尝试这样的事情：

import breeze.linalg.DenseVector

def compareAttrs(xs: Seq[Any], ys: Seq[Any]) = 
  DenseVector(xs.zip(ys).map{ case (x, y) => if (x == y) 1L else 0L}.toArray)

val result = graph.aggregateMessages[(Long, DenseVector[Long])](
  triplet => {
    val comparedAttrs = compareAttrs(triplet.dstAttr, triplet.srcAttr)
    triplet.sendToDst(1L, comparedAttrs)
    triplet.sendToSrc(1L, comparedAttrs)
  },
  { case ((cnt1, v1), (cnt2, v2)) => (cnt1 + cnt2, v1 + v2) }
)

result.mapValues(kv => (kv._2.map(_.toDouble) / kv._1.toDouble)).collect
// Array(
//   (1,DenseVector(0.5, 0.0, 0.5, 1.0, 1.0, 1.0, 1.0)),
//   (2,DenseVector(0.5, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0)), 
//   (3,DenseVector(0.0, 0.0, 0.5, 1.0, 1.0, 1.0, 1.0)))

如何计算graphx中邻居的顶点相似度

1 个答案: