diff --git a/examples/src/main/scala/org/apache/spark/examples/graphx/AnalyzeWikipedia.scala b/examples/src/main/scala/org/apache/spark/examples/graphx/AnalyzeWikipedia.scala
new file mode 100644
index 0000000000000..20f893d73b1d5
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/graphx/AnalyzeWikipedia.scala
@@ -0,0 +1,116 @@
+package org.apache.spark.examples.graphx
+
+import org.apache.spark._
+import org.apache.spark.graph._
+import org.apache.spark.graph.algorithms._
+import org.apache.spark.rdd.NewHadoopRDD
+import org.apache.hadoop.io.LongWritable
+import org.apache.hadoop.io.Text
+import org.apache.hadoop.conf.Configuration
+import org.apache.mahout.text.wikipedia._
+import org.apache.spark.rdd.RDD
+import java.util.Calendar
+import scala.math.Ordering.Implicits._
+
+
+object AnalyzeWikipedia extends Logging {
+
+  def main(args: Array[String]) = {
+
+
+
+
+    val host = args(0)
+    val fname = args(1)
+    // val numparts = {
+    //   if (args.length >= 3) {
+    //     args(2).toInt
+    //   } else {
+    //     64
+    //   }
+    // }
+    // val preformattedFname = args(2)
+
+   val serializer = "org.apache.spark.serializer.KryoSerializer"
+   System.setProperty("spark.serializer", serializer)
+   System.setProperty("spark.kryo.registrator", "org.apache.spark.graph.GraphKryoRegistrator")
+
+    val sc = new SparkContext(host, "AnalyzeWikipedia")
+    // val top10 = sc.parallelize(1 to 1000, 10).map(x => (x.toString, x)).top(10)(Ordering.by(_._2))
+
+
+    // val conf = new Configuration
+    // conf.set("key.value.separator.in.input.line", " ");
+    // conf.set("xmlinput.start", "<page>");
+    // conf.set("xmlinput.end", "</page>");
+
+    // val xmlRDD = sc.newAPIHadoopFile(fname, classOf[XmlInputFormat], classOf[LongWritable], classOf[Text], conf)
+    //   .map(stringify)
+
+    // println("XML pages: " + xmlRDD.count)
+    //   // .repartition(numparts)
+
+    // val wikiRDD = xmlRDD.map { raw => new WikiArticle(raw) }
+    //   .filter { art => art.relevant }
+
+    // println("Relevant pages: " + wikiRDD.count)
+
+    // val vertices: RDD[(Vid, String)] = wikiRDD.map { art => (art.vertexID, art.title) }
+    // val justVids = wikiRDD.map { art => art.vertexID }
+    // // println("taking top vids")
+    // // val topvids = justVids.top(10)
+    // // sc.stop()
+    // // System.exit(0)
+
+    // // val edges: RDD[Edge[Double]] = wikiRDD.flatMap { art => art.edges }
+    // val edges: RDD[Edge[Double]] = wikiRDD.flatMap { art => art.edges }
+    // println("Edges: " + edges.count)
+    // println("Creating graph: " + Calendar.getInstance().getTime())
+
+    // val g = Graph(vertices, edges)
+    // val g = Graph.fromEdges(edges, 1)
+    // val g = Graph(edges, 1)
+    val g = GraphLoader.edgeListAndVertexListFiles(sc, fname + "_edges", fname + "_vertices",
+      minEdgePartitions = 128).cache()
+    println("Triplets: " + g.triplets.count)
+
+    println("starting pagerank " + Calendar.getInstance().getTime())
+    val startTime = System.currentTimeMillis
+    val pr = PageRank.run(g, 20)
+
+    println("PR numvertices: " + pr.vertices.count + "\tOriginal numVertices " + g.vertices.count)
+    println("Pagerank runtime:    " + ((System.currentTimeMillis - startTime)/1000.0) + " seconds")
+    val prAndTitle = g.outerJoinVertices(pr.vertices)({(id: Vid, title: String, rank: Option[Double]) => (title, rank.getOrElse(0.0))})
+    println("finished join.")
+
+    val topArticles = prAndTitle.vertices.top(30)(Ordering.by((entry: (Vid, (String, Double))) => entry._2._2))
+    println("Top articles:\n" + topArticles.deep.mkString("\n"))
+    // for(v <- topArticles) {
+    //   println(v)
+    // }
+    val article_name = "JohnsHopkinsUniversity"
+    //
+    //Find relevant vertices
+    g.mapTriplets(e => {
+      if ((e.srcAttr contains article_name) || (e.dstAttr contains article_name)) { 1.0 }
+      else { e.attr }
+    })
+    val coarsenedGraph = g.contractEdges({ e => e.attr == 1.0 }, {et => et.srcAttr + " " + et.dstAttr },
+    { (v1: String , v2: String) => v1 + "\n" + v2 })
+    
+    // filter only vertices whose title contains JHU
+    val relevant = coarsenedGraph.vertices.filter( {case (vid: Vid, data: String) => data contains article_name}).collect
+    println("Articles matching " + article_name)
+    println(relevant.deep.mkString("New Article\n"))
+
+    sc.stop()
+  }
+
+
+  def stringify(tup: (org.apache.hadoop.io.LongWritable, org.apache.hadoop.io.Text)): String = {
+    tup._2.toString
+  }
+
+
+
+}
diff --git a/examples/src/main/scala/org/apache/spark/examples/graphx/PrePostProcessWiki.scala b/examples/src/main/scala/org/apache/spark/examples/graphx/PrePostProcessWiki.scala
new file mode 100644
index 0000000000000..a8cdba65b1cef
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/graphx/PrePostProcessWiki.scala
@@ -0,0 +1,174 @@
+package org.apache.spark.examples.graphx
+
+import org.apache.spark._
+import org.apache.spark.graphx._
+import org.apache.spark.graphx.lib._
+import org.apache.spark.graph.algorithms._
+import org.apache.spark.rdd.NewHadoopRDD
+import org.apache.hadoop.io.LongWritable
+import org.apache.hadoop.io.Text
+import org.apache.hadoop.conf.Configuration
+import org.apache.mahout.text.wikipedia._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.SparkContext._
+import java.util.Calendar
+import scala.math.Ordering.Implicits._
+import org.apache.spark.Logging
+import scala.collection.mutable
+
+
+object PrePostProcessWikipedia extends Logging {
+
+  def main(args: Array[String]) = {
+
+    val host = args(0)
+    val process = args(1)
+
+    val serializer = "org.apache.spark.serializer.KryoSerializer"
+    System.setProperty("spark.serializer", serializer)
+    System.setProperty("spark.kryo.registrator", "org.apache.spark.graph.GraphKryoRegistrator")
+
+    val sc = new SparkContext(host, "ETL")
+
+   val start = System.currentTimeMillis
+   process match {
+     case "pre" => {
+       val fname = args(2)
+       val outbase = args(3)
+       preProcess(sc, fname, outbase)
+     }
+     case "post" => {
+       val rankPath = args(2)
+       val attributePath = args(3)
+       val result = postProcess(sc, rankPath, attributePath)
+       logWarning(result)
+     }
+     case "graphx" => {
+       val rawData = args(2)
+       val result = graphx(sc, rawData)
+       logWarning(result)
+     }
+     case "prep" => {
+       val rawData = args(2)
+       val outBase = args(3)
+       prep(sc, rawData, outBase)
+     }
+
+     case _ => throw new IllegalArgumentException("Please provide a valid process")
+   }
+   logWarning(process + "\tTIMEX: " + (System.currentTimeMillis - start)/1000.0)
+   sc.stop()
+   System.exit(0)
+
+  }
+
+
+
+  def prep(sc: SparkContext, rawData: String, outBase: String) {
+
+    val conf = new Configuration
+    conf.set("key.value.separator.in.input.line", " ");
+    conf.set("xmlinput.start", "<page>");
+    conf.set("xmlinput.end", "</page>");
+
+    val vertPath = outBase + "_vertices"
+    val rankPath = outBase + "_ranks"
+    val xmlRDD = sc.newAPIHadoopFile(rawData, classOf[XmlInputFormat], classOf[LongWritable], classOf[Text], conf)
+      .map(stringify)
+    val wikiRDD = xmlRDD.map { raw => new WikiArticle(raw) }
+      .filter { art => art.relevant }.repartition(128)
+    val vertices: RDD[(Vid, String)] = wikiRDD.map { art => (art.vertexID, art.title) }
+    val verticesToSave = vertices.map {v => v._1 + "\t"+ v._2}
+    verticesToSave.saveAsTextFile(vertPath)
+    val edges: RDD[Edge[Double]] = wikiRDD.flatMap { art => art.edges }
+    val g = Graph(vertices, edges, partitionStrategy = EdgePartition1D)
+    val pr = PageRank.runStandalone(g, 0.01)
+    val prToSave = pr.map {v => v._1 + "\t"+ v._2}
+    prToSave.saveAsTextFile(rankPath)
+  }
+
+  def graphx(sc: SparkContext, rawData: String) {
+
+    val conf = new Configuration
+    conf.set("key.value.separator.in.input.line", " ");
+    conf.set("xmlinput.start", "<page>");
+    conf.set("xmlinput.end", "</page>");
+
+    val xmlRDD = sc.newAPIHadoopFile(rawData, classOf[XmlInputFormat], classOf[LongWritable], classOf[Text], conf)
+      .map(stringify)
+    val wikiRDD = xmlRDD.map { raw => new WikiArticle(raw) }
+      .filter { art => art.relevant }.repartition(128)
+    val vertices: RDD[(Vid, String)] = wikiRDD.map { art => (art.vertexID, art.title) }
+    val edges: RDD[Edge[Double]] = wikiRDD.flatMap { art => art.edges }
+    val g = Graph(vertices, edges, partitionStrategy = EdgePartition1D)
+    val resultG = pagerankConnComponentsAlt(4, g)
+    logWarning(s"Final graph has ${resultG.triplets.count} EDGES, ${resultG.vertices.count} VERTICES")
+//     val pr = PageRank.run(g, 20)
+//     val prAndTitle = g
+//       .outerJoinVertices(pr)({(id: Vid, title: String, rank: Option[Double]) => (title, rank.getOrElse(0.0))})
+//     val top20 = prAndTitle.vertices.top(20)(Ordering.by((entry: (Vid, (String, Double))) => entry._2._2))
+//     top20.mkString("\n")
+
+  }
+
+  def pagerankConnComponentsAlt(numRepetitions: Int, g: Graph[String, Double]): Graph[String, Double] = {
+    var currentGraph = g
+    for (i <- 0 to numRepetitions) {
+      val pr = PageRank.run(currentGraph, 20)
+      val prAndTitle = currentGraph
+        .outerJoinVertices(pr)({(id: Vid, title: String, rank: Option[Double]) => (title, rank.getOrElse(0.0))})
+      val top20 = prAndTitle.vertices.top(20)(Ordering.by((entry: (Vid, (String, Double))) => entry._2._2))
+      logWarning(s"Top20 for iteration $i:\n${top20.mkString("\n")}")
+      val top20verts = top20.map(_._1).toSet
+      // filter out top 20 vertices
+      val newGraph = currentGraph.subgraph(vpred = ((v, d) => !top20Verts.contains(v)))
+      val ccGraph = ConnectedComponents.run(newGraph)
+      val numCCs = ccGraph.vertices.aggregate(new mutable.HashSet())(((set, vtuple) => set += vtuple._2), ((set1, set2) => set1 union set2)).size
+      logWarning(s"Number of connected components for iteration $i: $numCCs")
+      // TODO will this result in too much memory overhead???
+      currentGraph = newGraph
+    }
+    currentGraph
+  }
+
+  // parse wikipedia xml dump and
+  def preProcess(sc: SparkContext, rawData: String, outBase: String) = {
+
+    val conf = new Configuration
+    conf.set("key.value.separator.in.input.line", " ");
+    conf.set("xmlinput.start", "<page>");
+    conf.set("xmlinput.end", "</page>");
+
+    val xmlRDD = sc.newAPIHadoopFile(rawData, classOf[XmlInputFormat], classOf[LongWritable], classOf[Text], conf)
+      .map(stringify)
+    val wikiRDD = xmlRDD.map { raw => new WikiArticle(raw) }
+      .filter { art => art.relevant }.repartition(128)
+    val vertices: RDD[(Vid, String)] = wikiRDD.map { art => (art.vertexID, art.title) }
+    val verticesToSave = vertices.map {v => v._1 + "\t"+ v._2}
+    verticesToSave.saveAsTextFile(outBase + "_vertices")
+    val edges: RDD[Edge[Double]] = wikiRDD.flatMap { art => art.edges }
+    val edgesToSave = edges.map(e => e.srcId + "\t" + e.dstId)
+    edgesToSave.saveAsTextFile(outBase + "_edges")
+
+  }
+
+
+  def postProcess(sc: SparkContext, rankPath: String, attrPath: String): String = {
+    val ranks = GraphLoader.loadVertices(sc, rankPath).map {v => (v._1, v._2.toDouble)}
+    val attrs = GraphLoader.loadVertices(sc, attrPath)
+
+    // slightly cheating, but not really
+    val ranksAndAttrs = ranks.join(attrs)
+    val top20 = ranksAndAttrs.top(20)(Ordering.by((entry: (Vid, (Double, String))) => entry._2._1))
+    top20.mkString("\n")
+  }
+
+
+
+  def stringify(tup: (org.apache.hadoop.io.LongWritable, org.apache.hadoop.io.Text)): String = {
+    tup._2.toString
+  }
+
+
+
+}
diff --git a/examples/src/main/scala/org/apache/spark/examples/graphx/WikiArticle.scala b/examples/src/main/scala/org/apache/spark/examples/graphx/WikiArticle.scala
new file mode 100644
index 0000000000000..e182462ae4cde
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/graphx/WikiArticle.scala
@@ -0,0 +1,98 @@
+package org.apache.spark.examples.graphx
+
+import java.util.regex.Pattern
+import org.apache.spark.graph._
+import java.util.regex.Matcher
+import scala.util.matching.Regex
+import scala.collection.mutable
+import scala.xml._
+import org.apache.spark.serializer.KryoRegistrator
+// import org.apache.spark.util.collection.OpenHashSet
+import scala.collection.immutable.HashSet
+import java.security.MessageDigest
+import java.nio.ByteBuffer
+
+
+class WikiArticle(wtext: String) extends Serializable {
+  @transient val links: Array[String] = WikiArticle.parseLinks(wtext)
+  @transient val neighbors = links.map(WikiArticle.titleHash).distinct
+  @transient lazy val redirect: Boolean = !WikiArticle.redirectPattern.findFirstIn(wtext).isEmpty
+  @transient lazy val stub: Boolean = !WikiArticle.stubPattern.findFirstIn(wtext).isEmpty
+  @transient lazy val disambig: Boolean = !WikiArticle.disambigPattern.findFirstIn(wtext).isEmpty
+  @transient lazy val tiXML = WikiArticle.titlePattern.findFirstIn(wtext).getOrElse("")
+  val title: String = {
+    try {
+      XML.loadString(tiXML).text
+    } catch {
+      case e => "" // don't use null because we get null pointer exceptions
+    }
+  }
+  val relevant: Boolean = !(redirect || stub || disambig || title == null)
+  val vertexID: Vid = WikiArticle.titleHash(title)
+  val edges: HashSet[Edge[Double]] = {
+    val temp = neighbors.map { n => Edge(vertexID, n, 1.0) }
+    val set = new HashSet[Edge[Double]]() ++ temp
+    set
+  }
+  // val edges: HashSet[(Vid, Vid)] = {
+  //   val temp = neighbors.map { n => (vertexID, n) }
+  //   val set = new HashSet[(Vid, Vid)]() ++ temp
+  //   set
+  // }
+}
+
+object WikiArticle {
+  @transient val titlePattern = "<title>(.*)<\\/title>".r
+  @transient val redirectPattern = "#REDIRECT\\s+\\[\\[(.*?)\\]\\]".r
+  @transient val disambigPattern = "\\{\\{disambig\\}\\}".r
+  @transient val stubPattern = "\\-stub\\}\\}".r
+  @transient val linkPattern = Pattern.compile("\\[\\[(.*?)\\]\\]", Pattern.MULTILINE) 
+
+  private def parseLinks(wt: String): Array[String] = {
+    val linkBuilder = new mutable.ArrayBuffer[String]()
+    val matcher: Matcher = linkPattern.matcher(wt)
+    while (matcher.find()) {
+      val temp: Array[String] = matcher.group(1).split("\\|")
+      if (temp != null && temp.length > 0) {
+        val link: String = temp(0)
+        if (link.contains(":") == false) {
+          linkBuilder += link
+        }
+      }
+    }
+    return linkBuilder.toArray
+  }
+
+  // substitute underscores for spaces and make lowercase
+  private def canonicalize(title: String): String = {
+    title.trim.toLowerCase.replace(" ", "_")
+  }
+
+  // Hash of the canonical article name. Used for vertex ID.
+  // TODO this should be a 64bit hash
+  private def titleHash(title: String): Vid = { math.abs(WikiArticle.myHashcode(canonicalize(title))) }
+
+  private def myHashcode(s: String): Long = {
+    // var h: Long = 1125899906842597L  // prime
+    // var h: Long = 4294967291L // prime
+  //   var h = 29
+  //   val len: Int = s.length
+  //   for (i<- 0 until len) {
+  //     h = 31*h + s.charAt(i)
+  //   }
+  //   h
+  //   // s.hashCode()
+  // }
+
+    val md: MessageDigest = MessageDigest.getInstance("MD5")
+    md.update(s.getBytes)
+    val result: Array[Byte] = md.digest()
+    val longResult = ByteBuffer.wrap(result).getLong
+    // shift result by 2
+    val retval = longResult >> 10
+    retval
+  }
+
+}
+
+
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala
index 1d029bf009e8c..5e343cd0f60f4 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala
@@ -314,8 +314,7 @@ object GraphImpl {
   def apply[VD: ClassTag, ED: ClassTag](
       vertices: RDD[(VertexId, VD)],
       edges: RDD[Edge[ED]],
-      defaultVertexAttr: VD): GraphImpl[VD, ED] =
-  {
+      defaultVertexAttr: VD): GraphImpl[VD, ED] = {
     val edgeRDD = createEdgeRDD(edges).cache()
 
     // Get the set of all vids
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 74bad66cfd018..ef8f8f8a8c716 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -302,6 +302,7 @@ object SparkBuild extends Build {
       "com.twitter"          %% "algebird-core"   % "0.1.11",
       "org.apache.hbase"     %  "hbase"           % "0.94.6" excludeAll(excludeNetty, excludeAsm),
       "org.apache.hbase" % "hbase" % HBASE_VERSION excludeAll(excludeNetty, excludeAsm),
+      "org.apache.mahout" % "mahout-integration" % "0.8",
       "org.apache.cassandra" % "cassandra-all" % "1.2.6"
         exclude("com.google.guava", "guava")
         exclude("com.googlecode.concurrentlinkedhashmap", "concurrentlinkedhashmap-lru")