graphx-lpa

1.LPA 标签传播算法,主要是顶点计算函数,选择label标签最多的项,更新顶点的属性。根据相应的业务,可以修改graphx的源码进行修改,改为我们业务中需要的标签值。由于LPA很难保证收敛,所以要设定迭代次数。

2.代码

object LabelPropagationAlgorithm {
  /**
    * Run static Label Propagation for detecting communities in networks.
    *
    * Each node in the network is initially assigned to its own community. At every superstep, nodes
    * send their community affiliation to all neighbors and update their state to the mode community
    * affiliation of incoming messages.
    *
    * LPA is a standard community detection algorithm for graphs. It is very inexpensive
    * computationally, although (1) convergence is not guaranteed and (2) one can end up with
    * trivial solutions (all nodes are identified into a single community).
    *
    * @tparam ED the edge attribute type (not used in the computation)
    * @param graph    the graph for which to compute the community affiliation
    * @param maxSteps the number of supersteps of LPA to be performed. Because this is a static
    *                 implementation, the algorithm will run for exactly this many supersteps.
    * @return a graph with vertex attributes containing the label of community affiliation
    */
  def run[VD, ED: ClassTag](graph: Graph[VD, ED], maxSteps: Int): Graph[VertexId, ED] = {
    require(maxSteps > 0, s"Maximum of steps must be greater than 0, but got ${maxSteps}")

    val lpaGraph = graph.mapVertices { case (vid, _) => vid }

    def sendMessage(e: EdgeTriplet[VertexId, ED]): Iterator[(VertexId, Map[VertexId, Long])] = {
      Iterator((e.srcId, Map(e.dstAttr -> 1L)), (e.dstId, Map(e.srcAttr -> 1L)))
    }

    def mergeMessage(count1: Map[VertexId, Long], count2: Map[VertexId, Long])
    : Map[VertexId, Long] = {
      (count1.keySet ++ count2.keySet).map { i =>
        val count1Val = count1.getOrElse(i, 0L)
        val count2Val = count2.getOrElse(i, 0L)
        i -> (count1Val + count2Val)
      }.toMap
    }

    def vertexProgram(vid: VertexId, attr: Long, message: Map[VertexId, Long]): VertexId = {
      if (message.isEmpty) attr else message.maxBy(_._2)._1
    }

    val initialMessage = Map[VertexId, Long]()
    Pregel(lpaGraph, initialMessage, maxIterations = maxSteps)(
      vprog = vertexProgram,
      sendMsg = sendMessage,
      mergeMsg = mergeMessage)
  }
}
### 使用 Spark GraphX 实现社交圈子检测 为了实现社交圈子(社区)检测,在 Spark GraphX 中可以利用 Pregel API 提供的功能来进行高效的图遍历和迭代计算[^1]。具体来说,可以通过以下方式构建解决方案: #### 构建图结构 首先需要创建表示社交网络的图对象。这通常涉及到定义顶点集合以及边集合。 ```scala import org.apache.spark.graphx._ // 假设已经有一个 RDD 表示用户之间的连接关系 (srcId, dstId) val edges: RDD[Edge[Int]] = ... // 创建默认属性为0的Graph实例 val graph: Graph[Int, Int] = Graph.fromEdges(edges, 0) ``` #### 应用LPA标签传播算法 Label Propagation Algorithm(LPA) 是一种简单而有效的用于发现社群的方法之一。该方法通过节点间传递标签并最终收敛到稳定状态来识别不同的群组成员。 ```scala def labelPropagation(graph: Graph[Int, _], maxIterations: Int): VertexRDD[(Int)] = { var g = graph.mapVertices((id, _) => id).cache() for (i <- 1 to maxIterations) { val msgRecv = g.aggregateMessages[Int]( triplet => { if(math.random < 0.5){ triplet.sendToSrc(triplet.dstAttr) }else{ triplet.sendToDst(triplet.srcAttr) } }, (a,b) => a+b ) g = g.joinVertices(msgRecv)((vid, vattr,msgs) => msgs match { case m if(m != null && m!=vattr )=> scala.util.Random.shuffle( Iterator.fill(1)(m)).next() //随机选取一个邻居作为新的label case _ => vattr }) println(s"Iteration $i completed.") } g.vertices.filter{case (_, attr) => true} } // 执行 LPA 并获取结果 val communities = labelPropagation(graph, 20) communities.collect().foreach(println(_)) ``` 此代码片段实现了基本版本的标签传播算法,其中每个节点将其当前持有的最大频率标签发送给相邻节点;接收方则更新自己的标签为其接收到的所有消息中最常见的那个。经过多轮迭代之后,具有相同标签的节点即属于同一个社团。
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