localClustering and eigenvectorCentrality Issues about sparkling-graph HOT 10 CLOSED

package org.learning.spark

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.storage.StorageLevel
import org.apache.spark.graphx.GraphLoader
import org.apache.spark.graphx.Graph
import org.apache.spark.rdd.RDD
import org.apache.spark.graphx._
import org.apache.spark.graphx.util.GraphGenerators._
import java.util.Calendar
import ml.sparkling.graph.operators.OperatorsDSL._
import ml.sparkling.graph.api.operators.measures.VertexMeasureConfiguration
import org.apache.spark.sql.SQLContext._
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.SQLContext
import org.apache.log4j.{Level, Logger}

object JustForTrying {
  def main(args: Array[String]) {
    
    Logger.getLogger("org").setLevel(Level.OFF)
    Logger.getLogger("akka").setLevel(Level.OFF)
    val master = args(0)
    val sourceGraph = args(1)
    val compGraph = args(2)
    val run = args(3)
    val numPart = args(4)
    val jobName = args(5)
    val runFull = args(6)
    
    val sparkConf: SparkConf = new SparkConf().setAppName(jobName.toString()).setMaster(master)
   
    val sc:SparkContext=new SparkContext(sparkConf)
    val sqlContext = new org.apache.spark.sql.SQLContext(sc)
    import sqlContext.implicits._

      println("Comparing Two Graphs")
      
      val graph: Graph[Int, Int] = GraphLoader.edgeListFile(sc, sourceGraph, canonicalOrientation = true, numEdgePartitions = numPart.toInt, edgeStorageLevel = StorageLevel.MEMORY_AND_DISK_SER, vertexStorageLevel = StorageLevel.MEMORY_AND_DISK_SER)
        .partitionBy(PartitionStrategy.RandomVertexCut, numPart.toInt).groupEdges((a, b) => a)
      
     
      // Local Clustering Score
    val clusteringGraph: Graph[Double, Int] = graph.localClustering(VertexMeasureConfiguration(treatAsUndirected=true))
    val localCentralityRDD: VertexRDD[Double] = clusteringGraph.vertices
    // Eigen Vector Score
    val eigenvectorRDD: VertexRDD[Double] = graph.eigenvectorCentrality(VertexMeasureConfiguration(treatAsUndirected=true)).vertices
   
  sc.stop()   
  
  }
}

  <dependency>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-core_2.10</artifactId>
     <version>2.2.0</version>
  </dependency>

  <dependency>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-sql_2.11</artifactId>
     <version>2.1.0</version>
     <scope>provided</scope>
  </dependency>

  <dependency>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-graphx_2.10</artifactId>
     <version>2.2.0</version>
  </dependency>
  
  <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-sql_2.11</artifactId>
      <version>2.2.0</version>
  </dependency>
  
  <dependency>
       <groupId>org.apache.spark</groupId>
       <artifactId>spark-mllib_2.10</artifactId>
       <version>1.6.0</version>
  </dependency>

<dependency>
    <groupId>ml.sparkling</groupId>
    <artifactId>sparkling-graph-api_2.11</artifactId>
    <version>0.0.7</version>
</dependency>

<dependency>
    <groupId>ml.sparkling</groupId>
    <artifactId>sparkling-graph-operators_2.11</artifactId>
    <version>0.0.7</version>
</dependency>