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initial version of LPA
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haroldsultan committed May 28, 2014
1 parent 82eadc3 commit 9830342
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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/

package org.apache.spark.graphx.lib

import scala.reflect.ClassTag
import org.apache.spark.graphx._

/** LPA algorithm. */
object LPA {
/**
* Run LPA (label propogation algorithm) for detecting communities in networks using the pregel framework.
*
* Each node in the network is initially assigned to its own community. At every super step
* nodes send their community affiliation to all neighbors and update their state to the mode
* community affiliation of incomming 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 VD the vertex attribute type (discarded in the computation)
* @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
*
* @return a graph with vertex attributes containing the label of community affiliation
*/
def run[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED], maxSteps: Int): Graph[VertexId, Long]{
val lpaGraph = graph.mapVertices { case (vid, _) => vid }
def sendMessage(edge: EdgeTriplet[VertexId, ED]) = {
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])={
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)
}
}

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