diff --git a/.gitignore b/.gitignore
index 0d20b64..69fa6ec 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,14 @@
 *.pyc
+.DS_Store
+target
+bin
+build
+.gradle
+*.iml
+*.ipr
+*.iws
+*.log
+.classpath
+.project
+.settings
+.idea
\ No newline at end of file
diff --git a/LICENSE.md b/LICENSE.md
new file mode 100644
index 0000000..af4a91f
--- /dev/null
+++ b/LICENSE.md
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2016 Aditya Grover
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/emb/karate.emb b/emb/karate.emb
index 0c3c45d..8f7edb4 100644
--- a/emb/karate.emb
+++ b/emb/karate.emb
@@ -1,35 +1,35 @@
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diff --git a/node2vec_spark/README.md b/node2vec_spark/README.md
new file mode 100644
index 0000000..43f6684
--- /dev/null
+++ b/node2vec_spark/README.md
@@ -0,0 +1,139 @@
+# node2vec on spark
+
+This library is a implementation using scala for running on spark of *node2vec* as described in the paper:
+> node2vec: Scalable Feature Learning for Networks.
+> Aditya Grover and Jure Leskovec.
+> Knowledge Discovery and Data Mining, 2016.
+> <Insert paper link>
+
+The *node2vec* algorithm learns continuous representations for nodes in any (un)directed, (un)weighted graph. Please check the [project page](https://snap.stanford.edu/node2vec/) for more details. 
+
+
+### Building node2vec_spark
+**In order to build node2vec_spark, use the following:**
+
+```
+$ git clone https://github.com/Skarface-/node2vec.git
+$ mvn clean package
+```
+
+**and requires:**<br/>
+Maven 3.0.5 or newer<br/>
+Java 7+<br/>
+Scala 2.10 or newer.
+
+This will produce jar file in "node2vec_spark/target/"
+
+### Examples
+This library has two functions: *randomwalk* and *embedding*. <br/> 
+These were described in these papers [node2vec: Scalable Feature Learning for Networks](http://arxiv.org/abs/1607.00653) and [Efficient Estimation of Word Representations in Vector Space](https://arxiv.org/abs/1301.3781).
+
+### Random walk
+Example:
+	
+	./spark-submit --class com.navercorp.Main \ 
+				   ./node2vec_spark/target/node2vec-0.0.1-SNAPSHOT.jar \
+				   --cmd randomwalk --p 100.0 --q 100.0 --walkLength 40 \
+				   --input <input> --output <output>
+
+#### Options
+Invoke a command without arguments to list available arguments and their default values:
+
+```
+--cmd COMMAND
+	Functions: randomwalk or embedding. If you want to execute all functions "randomwalk" and "embedding" sequentially input "node2vec". Default "node2vec"
+--input [INPUT]
+	Input edgelist path. The supported input format is an edgelist: "node1_id_int node2_id_int <weight_float, optional>"
+--output [OUTPUT]
+	Random paths path.
+--walkLength WALK_LENGTH
+	Length of walk per source. Default is 80.
+--numWalks NUM_WALKS
+	Number of walks per source. Default is 10.
+--p P
+	Return hyperparaemter. Default is 1.0.
+--q Q
+	Inout hyperparameter. Default is 1.0.
+--weighted Boolean
+	Specifying (un)weighted. Default is true.
+--directed Boolean
+	Specifying (un)directed. Default is false.
+--degree UPPER_BOUND_OF_NUMBER_OF_NEIGHBORS
+	Specifying upper bound of number of neighbors. Default is 30.
+--indexed Boolean
+	Specifying whether nodes in edgelist are indexed or not. Default is true.
+```
+
+* If "indexed" is set to false, *node2vec_spark* index nodes in input edgelist, example: <br/>
+  **unindexed edgelist:**<br/>
+  node1 	node2 	1.0<br/>
+  node2 	node7 	1.0<br/>
+  
+  **indexed:**<br/>
+  1 	2 	1.0<br/>
+  2 	3 	1.0<br/>
+
+  1 	node1<br/>
+  2 	node2<br/>
+  3 	node7
+
+#### Input
+The supported input format is an edgelist:
+
+	node1_id_int 	node2_id_int 	<weight_float, optional>
+	or
+	node1_str 	node2_str 	<weight_float, optional>, Please set the option "indexed" to false
+
+
+#### Output
+The output file (number of nodes)*numWalks random paths as follows:
+
+	src_node_id_int 	node1_id_int 	node2_id_int 	... 	noden_id_int
+
+
+### Embedding random paths
+Example:
+	
+	./spark-submit --class com.navercorp.Main \
+				   ./node2vec_spark/target/node2vec-0.0.1-SNAPSHOT.jar \
+				   --cmd embedding --dim 50 --iter 20 \
+				   --input <input> --nodePath <node2id_path> --output <output>
+
+#### Options
+Invoke a command without arguments to list available arguments and their default values:
+
+```
+--cmd COMMAND
+	embedding. If you want to execute sequentially all functions: "randomwalk" and "embedding", input "node2vec". default "node2vec"
+--input [INPUT]
+	Input random paths. The supported input format is an random paths: "src_node_id_int node1_id_int ... noden_id_int"
+--output [OUTPUT]
+	word2vec model(.bin) and embeddings(.emb).
+--nodePath [NODE\_PATH]
+	Input node2index path. The supported input format: "node1_str node1_id_int"
+--iter ITERATION
+	Number of epochs in SGD. Default 10.
+--dim DIMENSION
+	Number of dimensions. Default is 128.
+--window WINDOW_SIZE
+	Context size for optimization. Default is 10.
+
+```
+
+#### Input
+The supported input format is an random paths:
+
+	src_node_id_int 	node1_id_int 	... 	noden_id_int
+
+#### Output
+The output files are **embeddings and word2vec model.** The embeddings file has the following format: 
+
+	node1_str 	dim1 dim2 ... dimd
+
+where dim1, ... , dimd is the d-dimensional representation learned by word2vec.
+
+the output file *word2vec model* has the spark word2vec model format. please reference to https://spark.apache.org/docs/1.5.2/mllib-feature-extraction.html#word2vec
+
+## References
+1. [node2vec: Scalable Feature Learning for Networks](http://arxiv.org/abs/1607.00653)
+2. [Efficient Estimation of Word Representations in Vector Space](https://arxiv.org/abs/1301.3781)
\ No newline at end of file
diff --git a/node2vec_spark/pom.xml b/node2vec_spark/pom.xml
new file mode 100644
index 0000000..b958576
--- /dev/null
+++ b/node2vec_spark/pom.xml
@@ -0,0 +1,129 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0%20http://maven.apache.org/maven-v4_0_0.xsd">
+
+    <modelVersion>4.0.0</modelVersion>
+    
+    <groupId>com.navercorp</groupId>
+    <artifactId>node2vec</artifactId>
+    <packaging>jar</packaging>
+    <version>0.0.1-SNAPSHOT</version>
+    
+    <name>node2vec_spark</name>
+    <url>http://snap.stanford.edu/node2vec/</url>
+
+    <properties>
+        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+        <shadedClassifier>bin</shadedClassifier>
+        <maven-shade-plugin.version>2.4.3</maven-shade-plugin.version>
+        <exec-maven-plugin.version>1.4.0</exec-maven-plugin.version>
+        <java.version>1.7</java.version>
+        <scala.binary.version>2.10</scala.binary.version>
+    </properties>
+
+    <build>
+        <plugins>
+            <plugin>
+                <groupId>org.scala-tools</groupId>
+                <artifactId>maven-scala-plugin</artifactId>
+                <version>2.15.2</version>
+                <executions>
+                    <execution>
+                        <goals>
+                            <goal>compile</goal>
+                        </goals>
+                    </execution>
+                </executions>
+            </plugin>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-dependency-plugin</artifactId>
+                <version>2.4</version>
+                <executions>
+                    <execution>
+                        <id>copy-dependencies</id>
+                        <phase>package</phase>
+                        <goals>
+                            <goal>copy-dependencies</goal>
+                        </goals>
+                        <configuration>
+                            <outputDirectory>${project.build.directory}/lib</outputDirectory>
+                        </configuration>
+                    </execution>
+                </executions>
+            </plugin>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-shade-plugin</artifactId>
+                <version>1.6</version>
+                <executions>
+                    <execution>
+                        <phase>package</phase>
+                        <goals>
+                            <goal>shade</goal>
+                        </goals>
+                    </execution>
+                </executions>
+            </plugin>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-compiler-plugin</artifactId>
+                <version>2.3.2</version>
+                <configuration>
+                    <source>1.7</source>
+                    <target>1.7</target>
+                    <encoding>UTF-8</encoding>
+                </configuration>
+            </plugin>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-surefire-plugin</artifactId>
+                <configuration>
+                    <skip>false</skip>
+                </configuration>
+            </plugin>
+        </plugins>
+    </build>
+    
+    <dependencies>
+        <dependency>
+            <groupId>org.apache.hadoop</groupId>
+            <artifactId>hadoop-hdfs</artifactId>
+            <version>2.7.1</version>
+        </dependency>
+        <dependency>
+            <groupId>org.scala-lang</groupId>
+            <artifactId>scala-library</artifactId>
+            <version>${scala.binary.version}.5</version>
+            <scope>provided</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.apache.spark</groupId>
+            <artifactId>spark-core_${scala.binary.version}</artifactId>
+            <version>1.6.1</version>
+            <scope>provided</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.apache.spark</groupId>
+            <artifactId>spark-mllib_${scala.binary.version}</artifactId>
+            <version>1.6.1</version>
+            <scope>provided</scope>
+        </dependency>
+        <dependency>
+            <groupId>com.github.scopt</groupId>
+            <artifactId>scopt_${scala.binary.version}</artifactId>
+            <version>3.3.0</version>
+            <exclusions>
+                <exclusion>
+                    <groupId>org.scala-lang</groupId>
+                    <artifactId>scala-library</artifactId>
+                </exclusion>
+            </exclusions>
+        </dependency>
+        <dependency>
+            <groupId>com.google.guava</groupId>
+            <artifactId>guava</artifactId>
+            <version>19.0</version>
+        </dependency>
+    </dependencies>
+
+</project>
diff --git a/node2vec_spark/src/main/resources/log4j2.properties b/node2vec_spark/src/main/resources/log4j2.properties
new file mode 100644
index 0000000..d941e1b
--- /dev/null
+++ b/node2vec_spark/src/main/resources/log4j2.properties
@@ -0,0 +1,9 @@
+
+appender.out.type = Console
+appender.out.name = out
+appender.out.layout.type = PatternLayout
+appender.out.layout.pattern = [%30.30t] %-30.30c{1} %-5p %m%n
+logger.springframework.name = org.springframework
+logger.springframework.level = WARN
+rootLogger.level = INFO
+rootLogger.appenderRef.out.ref = out
diff --git a/node2vec_spark/src/main/scala/com/navercorp/Main.scala b/node2vec_spark/src/main/scala/com/navercorp/Main.scala
new file mode 100644
index 0000000..f3494e5
--- /dev/null
+++ b/node2vec_spark/src/main/scala/com/navercorp/Main.scala
@@ -0,0 +1,119 @@
+package com.navercorp
+
+import java.io.Serializable
+import org.apache.spark.{SparkContext, SparkConf}
+import scopt.OptionParser
+import com.navercorp.lib.AbstractParams
+
+object Main {
+  object Command extends Enumeration {
+    type Command = Value
+    val node2vec, randomwalk, embedding = Value
+  }
+  import Command._
+
+  case class Params(iter: Int = 10,
+                    lr: Double = 0.025,
+                    numPartition: Int = 10,
+                    dim: Int = 128,
+                    window: Int = 10,
+                    walkLength: Int = 80,
+                    numWalks: Int = 10,
+                    p: Double = 1.0,
+                    q: Double = 1.0,
+                    weighted: Boolean = true,
+                    directed: Boolean = false,
+                    degree: Int = 30,
+                    indexed: Boolean = true,
+                    nodePath: String = null,
+                    input: String = null,
+                    output: String = null,
+                    cmd: Command = Command.node2vec) extends AbstractParams[Params] with Serializable
+  val defaultParams = Params()
+  
+  val parser = new OptionParser[Params]("Node2Vec_Spark") {
+    head("Main")
+    opt[Int]("walkLength")
+            .text(s"walkLength: ${defaultParams.walkLength}")
+            .action((x, c) => c.copy(walkLength = x))
+    opt[Int]("numWalks")
+            .text(s"numWalks: ${defaultParams.numWalks}")
+            .action((x, c) => c.copy(numWalks = x))
+    opt[Double]("p")
+            .text(s"return parameter p: ${defaultParams.p}")
+            .action((x, c) => c.copy(p = x))
+    opt[Double]("q")
+            .text(s"in-out parameter q: ${defaultParams.q}")
+            .action((x, c) => c.copy(q = x))
+    opt[Boolean]("weighted")
+            .text(s"weighted: ${defaultParams.weighted}")
+            .action((x, c) => c.copy(weighted = x))
+    opt[Boolean]("directed")
+            .text(s"directed: ${defaultParams.directed}")
+            .action((x, c) => c.copy(directed = x))
+    opt[Int]("degree")
+            .text(s"degree: ${defaultParams.degree}")
+            .action((x, c) => c.copy(degree = x))
+    opt[Boolean]("indexed")
+            .text(s"Whether nodes are indexed or not: ${defaultParams.indexed}")
+            .action((x, c) => c.copy(indexed = x))
+    opt[String]("nodePath")
+            .text("Input node2index file path: empty")
+            .action((x, c) => c.copy(nodePath = x))
+    opt[String]("input")
+            .required()
+            .text("Input edge file path: empty")
+            .action((x, c) => c.copy(input = x))
+    opt[String]("output")
+            .required()
+            .text("Output path: empty")
+            .action((x, c) => c.copy(output = x))
+    opt[String]("cmd")
+            .required()
+            .text(s"command: ${defaultParams.cmd.toString}")
+            .action((x, c) => c.copy(cmd = Command.withName(x)))
+    note(
+      """
+        |For example, the following command runs this app on a synthetic dataset:
+        |
+        | bin/spark-submit --class com.nhn.sunny.vegapunk.ml.model.Node2vec \
+      """.stripMargin +
+              s"|   --lr ${defaultParams.lr}" +
+              s"|   --iter ${defaultParams.iter}" +
+              s"|   --numPartition ${defaultParams.numPartition}" +
+              s"|   --dim ${defaultParams.dim}" +
+              s"|   --window ${defaultParams.window}" +
+              s"|   --input <path>" +
+              s"|   --node <nodeFilePath>" +
+              s"|   --output <path>"
+    )
+  }
+  
+  def main(args: Array[String]) = {
+    parser.parse(args, defaultParams).map { param =>
+      val conf = new SparkConf().setAppName("Node2Vec")
+      val context: SparkContext = new SparkContext(conf)
+      
+      Node2vec.setup(context, param)
+      
+      param.cmd match {
+        case Command.node2vec => Node2vec.load()
+                                         .initTransitionProb()
+                                         .randomWalk()
+                                         .embedding()
+                                         .save()
+        case Command.randomwalk => Node2vec.load()
+                                           .initTransitionProb()
+                                           .randomWalk()
+                                           .saveRandomPath()
+        case Command.embedding => {
+          val randomPaths = Word2vec.setup(context, param).read(param.input)
+          Word2vec.fit(randomPaths).save(param.output)
+          Node2vec.loadNode2Id(param.nodePath).saveVectors()
+        }
+      }
+    } getOrElse {
+      sys.exit(1)
+    }
+  }
+}
diff --git a/node2vec_spark/src/main/scala/com/navercorp/Node2vec.scala b/node2vec_spark/src/main/scala/com/navercorp/Node2vec.scala
new file mode 100644
index 0000000..07ec21a
--- /dev/null
+++ b/node2vec_spark/src/main/scala/com/navercorp/Node2vec.scala
@@ -0,0 +1,281 @@
+package com.navercorp
+
+
+import java.io.Serializable
+import scala.util.Try
+import scala.collection.mutable.ArrayBuffer
+import org.slf4j.{Logger, LoggerFactory}
+import org.apache.spark.SparkContext
+import org.apache.spark.rdd.RDD
+import org.apache.spark.graphx.{EdgeTriplet, Graph, _}
+import com.navercorp.graph.{GraphOps, EdgeAttr, NodeAttr}
+
+object Node2vec extends Serializable {
+  lazy val logger: Logger = LoggerFactory.getLogger(getClass.getName);
+  
+  var context: SparkContext = null
+  var config: Main.Params = null
+  var node2id: RDD[(String, Long)] = null
+  var indexedEdges: RDD[Edge[EdgeAttr]] = _
+  var indexedNodes: RDD[(VertexId, NodeAttr)] = _
+  var graph: Graph[NodeAttr, EdgeAttr] = _
+  var randomWalkPaths: RDD[(Long, ArrayBuffer[Long])] = null
+
+  def setup(context: SparkContext, param: Main.Params): this.type = {
+    this.context = context
+    this.config = param
+    
+    this
+  }
+  
+  def load(): this.type = {
+    val bcMaxDegree = context.broadcast(config.degree)
+    val bcEdgeCreator = config.directed match {
+      case true => context.broadcast(GraphOps.createDirectedEdge)
+      case false => context.broadcast(GraphOps.createUndirectedEdge)
+    }
+    
+    val inputTriplets: RDD[(Long, Long, Double)] = config.indexed match {
+      case true => readIndexedGraph(config.input)
+      case false => indexingGraph(config.input)
+    }
+    
+    indexedNodes = inputTriplets.flatMap { case (srcId, dstId, weight) =>
+      bcEdgeCreator.value.apply(srcId, dstId, weight)
+    }.reduceByKey(_++_).map { case (nodeId, neighbors: Array[(VertexId, Double)]) =>
+      var neighbors_ = neighbors
+      if (neighbors_.length > bcMaxDegree.value) {
+        neighbors_ = neighbors.sortWith{ case (left, right) => left._2 > right._2 }.slice(0, bcMaxDegree.value)
+      }
+        
+      (nodeId, NodeAttr(neighbors = neighbors_.distinct))
+    }.repartition(200).cache
+    
+    indexedEdges = indexedNodes.flatMap { case (srcId, clickNode) =>
+      clickNode.neighbors.map { case (dstId, weight) =>
+          Edge(srcId, dstId, EdgeAttr())
+      }
+    }.repartition(200).cache
+    
+    this
+  }
+  
+  def initTransitionProb(): this.type = {
+    val bcP = context.broadcast(config.p)
+    val bcQ = context.broadcast(config.q)
+    
+    graph = Graph(indexedNodes, indexedEdges)
+            .mapVertices[NodeAttr] { case (vertexId, clickNode) =>
+              val (j, q) = GraphOps.setupAlias(clickNode.neighbors)
+              val nextNodeIndex = GraphOps.drawAlias(j, q)
+              clickNode.path = Array(vertexId, clickNode.neighbors(nextNodeIndex)._1)
+              
+              clickNode
+            }
+            .mapTriplets { edgeTriplet: EdgeTriplet[NodeAttr, EdgeAttr] =>
+              val (j, q) = GraphOps.setupEdgeAlias(bcP.value, bcQ.value)(edgeTriplet.srcId, edgeTriplet.srcAttr.neighbors, edgeTriplet.dstAttr.neighbors)
+              edgeTriplet.attr.J = j
+              edgeTriplet.attr.q = q
+              edgeTriplet.attr.dstNeighbors = edgeTriplet.dstAttr.neighbors.map(_._1)
+              
+              edgeTriplet.attr
+            }.cache
+    
+    this
+  }
+  
+  def randomWalk(): this.type = {
+    val edge2attr = graph.triplets.map { edgeTriplet =>
+      (s"${edgeTriplet.srcId}${edgeTriplet.dstId}", edgeTriplet.attr)
+    }.repartition(200).cache
+    edge2attr.first
+    
+    for (iter <- 0 until config.numWalks) {
+      var prevWalk: RDD[(Long, ArrayBuffer[Long])] = null
+      var randomWalk = graph.vertices.map { case (nodeId, clickNode) =>
+        val pathBuffer = new ArrayBuffer[Long]()
+        pathBuffer.append(clickNode.path:_*)
+        (nodeId, pathBuffer) 
+      }.cache
+      var activeWalks = randomWalk.first
+      graph.unpersist(blocking = false)
+      graph.edges.unpersist(blocking = false)
+      for (walkCount <- 0 until config.walkLength) {
+        prevWalk = randomWalk
+        randomWalk = randomWalk.map { case (srcNodeId, pathBuffer) =>
+          val prevNodeId = pathBuffer(pathBuffer.length - 2)
+          val currentNodeId = pathBuffer.last
+
+          (s"$prevNodeId$currentNodeId", (srcNodeId, pathBuffer))
+        }.join(edge2attr).map { case (edge, ((srcNodeId, pathBuffer), attr)) =>
+          try {
+            val nextNodeIndex = GraphOps.drawAlias(attr.J, attr.q)
+            val nextNodeId = attr.dstNeighbors(nextNodeIndex)
+            pathBuffer.append(nextNodeId)
+
+            (srcNodeId, pathBuffer)
+          } catch {
+            case e: Exception => throw new RuntimeException(e.getMessage)
+          }
+        }.cache
+
+        activeWalks = randomWalk.first()
+        prevWalk.unpersist(blocking=false)
+      }
+
+
+      if (randomWalkPaths != null) {
+        val prevRandomWalkPaths = randomWalkPaths
+        randomWalkPaths = randomWalkPaths.union(randomWalk).cache()
+        randomWalkPaths.first
+        prevRandomWalkPaths.unpersist(blocking = false)
+      } else {
+        randomWalkPaths = randomWalk
+      }
+    }
+    
+    this
+  }
+  
+  def embedding(): this.type = {
+    val randomPaths = randomWalkPaths.map { case (vertexId, pathBuffer) =>
+      Try(pathBuffer.map(_.toString).toIterable).getOrElse(null)
+    }.filter(_!=null)
+    
+    Word2vec.setup(context, config).fit(randomPaths)
+    
+    this
+  }
+  
+  def save(): this.type = {
+    this.saveRandomPath()
+        .saveModel()
+        .saveVectors()
+  }
+  
+  def saveRandomPath(): this.type = {
+    randomWalkPaths
+            .map { case (vertexId, pathBuffer) =>
+              Try(pathBuffer.mkString("\t")).getOrElse(null)
+            }
+            .filter(x => x != null && x.replaceAll("\\s", "").length > 0)
+            .repartition(200)
+            .saveAsTextFile(config.output)
+    
+    this
+  }
+  
+  def saveModel(): this.type = {
+    Word2vec.save(config.output)
+    
+    this
+  }
+  
+  def saveVectors(): this.type = {
+    val node2vector = context.parallelize(Word2vec.getVectors.toList)
+            .map { case (nodeId, vector) =>
+              (nodeId.toLong, vector.mkString(","))
+            }
+    
+    if (this.node2id != null) {
+      val id2Node = this.node2id.map{ case (strNode, index) =>
+        (index, strNode)
+      }
+      
+      node2vector.join(id2Node)
+              .map { case (nodeId, (vector, name)) => s"$name\t$vector" }
+              .repartition(200)
+              .saveAsTextFile(s"${config.output}.emb")
+    } else {
+      node2vector.map { case (nodeId, vector) => s"$nodeId\t$vector" }
+              .repartition(200)
+              .saveAsTextFile(s"${config.output}.emb")
+    }
+    
+    this
+  }
+  
+  def cleanup(): this.type = {
+    node2id.unpersist(blocking = false)
+    indexedEdges.unpersist(blocking = false)
+    indexedNodes.unpersist(blocking = false)
+    graph.unpersist(blocking = false)
+    randomWalkPaths.unpersist(blocking = false)
+    
+    this
+  }
+
+  def loadNode2Id(node2idPath: String): this.type = {
+    try {
+      this.node2id = context.textFile(config.nodePath).map { node2index =>
+        val Array(strNode, index) = node2index.split("\\s")
+        (strNode, index.toLong)
+      }
+    } catch {
+      case e: Exception => logger.info("Failed to read node2index file.")
+      this.node2id = null
+    }
+
+    this
+  }
+  
+  def readIndexedGraph(tripletPath: String) = {
+    val bcWeighted = context.broadcast(config.weighted)
+    
+    val rawTriplets = context.textFile(tripletPath)
+    if (config.nodePath == null) {
+      this.node2id = createNode2Id(rawTriplets.map { triplet =>
+        val parts = triplet.split("\\s")
+        (parts.head, parts(1), -1)
+      })
+    } else {
+      loadNode2Id(config.nodePath)
+    }
+
+    rawTriplets.map { triplet =>
+      val parts = triplet.split("\\s")
+      val weight = bcWeighted.value match {
+        case true => Try(parts.last.toDouble).getOrElse(1.0)
+        case false => 1.0
+      }
+
+      (parts.head.toLong, parts(1).toLong, weight)
+    }
+  }
+  
+
+  def indexingGraph(rawTripletPath: String): RDD[(Long, Long, Double)] = {
+    val rawEdges = context.textFile(rawTripletPath).map { triplet =>
+      val parts = triplet.split("\\s")
+
+      Try {
+        (parts.head, parts(1), Try(parts.last.toDouble).getOrElse(1.0))
+      }.getOrElse(null)
+    }.filter(_!=null)
+    
+    this.node2id = createNode2Id(rawEdges)
+    
+    rawEdges.map { case (src, dst, weight) =>
+      (src, (dst, weight))
+    }.join(node2id).map { case (src, (edge: (String, Double), srcIndex: Long)) =>
+      try {
+        val (dst: String, weight: Double) = edge
+        (dst, (srcIndex, weight))
+      } catch {
+        case e: Exception => null
+      }
+    }.filter(_!=null).join(node2id).map { case (dst, (edge: (Long, Double), dstIndex: Long)) =>
+      try {
+        val (srcIndex, weight) = edge
+        (srcIndex, dstIndex, weight)
+      } catch {
+        case e: Exception => null
+      }
+    }.filter(_!=null)
+  }
+  
+  def createNode2Id[T <: Any](triplets: RDD[(String, String, T)]) = triplets.flatMap { case (src, dst, weight) =>
+    Try(Array(src, dst)).getOrElse(Array.empty[String])
+  }.distinct().zipWithIndex()
+
+}
diff --git a/node2vec_spark/src/main/scala/com/navercorp/Word2vec.scala b/node2vec_spark/src/main/scala/com/navercorp/Word2vec.scala
new file mode 100644
index 0000000..aa209cf
--- /dev/null
+++ b/node2vec_spark/src/main/scala/com/navercorp/Word2vec.scala
@@ -0,0 +1,55 @@
+package com.navercorp
+
+import org.apache.spark.SparkContext
+import org.apache.spark.mllib.feature.{Word2Vec, Word2VecModel}
+import org.apache.spark.rdd.RDD
+
+object Word2vec extends Serializable {
+  var context: SparkContext = null
+  var word2vec = new Word2Vec()
+  var model: Word2VecModel = null
+  
+  def setup(context: SparkContext, param: Main.Params): this.type = {
+    this.context = context
+    /**
+      * model = sg
+      * update = hs
+      */
+    word2vec.setLearningRate(param.lr)
+            .setNumIterations(param.iter)
+            .setNumPartitions(param.numPartition)
+            .setMinCount(0)
+            .setVectorSize(param.dim)
+
+    val word2vecWindowField = word2vec.getClass.getDeclaredField("org$apache$spark$mllib$feature$Word2Vec$$window")
+    word2vecWindowField.setAccessible(true)
+    word2vecWindowField.setInt(word2vec, param.window)
+    
+    this
+  }
+  
+  def read(path: String): RDD[Iterable[String]] = {
+    context.textFile(path).repartition(200).map(_.split("\\s").toSeq)
+  }
+  
+  def fit(input: RDD[Iterable[String]]): this.type = {
+    model = word2vec.fit(input)
+    
+    this
+  }
+  
+  def save(outputPath: String): this.type = {
+    model.save(context, s"$outputPath.bin")
+    this
+  }  
+  
+  def load(path: String): this.type = {
+    model = Word2VecModel.load(context, path)
+    
+    this
+  }
+  
+  def getVectors = this.model.getVectors
+  
+}
+
diff --git a/node2vec_spark/src/main/scala/com/navercorp/graph/GraphOps.scala b/node2vec_spark/src/main/scala/com/navercorp/graph/GraphOps.scala
new file mode 100644
index 0000000..960fa8c
--- /dev/null
+++ b/node2vec_spark/src/main/scala/com/navercorp/graph/GraphOps.scala
@@ -0,0 +1,69 @@
+package com.navercorp.graph
+
+import scala.collection.mutable.ArrayBuffer
+
+object GraphOps {
+  def setupAlias(nodeWeights: Array[(Long, Double)]): (Array[Int], Array[Double]) = {
+    val K = nodeWeights.length
+    val J = Array.fill(K)(0)
+    val q = Array.fill(K)(0.0)
+
+    val smaller = new ArrayBuffer[Int]()
+    val larger = new ArrayBuffer[Int]()
+
+    val sum = nodeWeights.map(_._2).sum
+    nodeWeights.zipWithIndex.foreach { case ((nodeId, weight), i) =>
+      q(i) = K * weight / sum
+      if (q(i) < 1.0) {
+        smaller.append(i)
+      } else {
+        larger.append(i)
+      }
+    }
+
+    while (smaller.nonEmpty && larger.nonEmpty) {
+      val small = smaller.remove(smaller.length - 1)
+      val large = larger.remove(larger.length - 1)
+
+      J(small) = large
+      q(large) = q(large) + q(small) - 1.0
+      if (q(large) < 1.0) smaller.append(large)
+      else larger.append(large)
+    }
+
+    (J, q)
+  }
+
+  def setupEdgeAlias(p: Double = 1.0, q: Double = 1.0)(srcId: Long, srcNeighbors: Array[(Long, Double)], dstNeighbors: Array[(Long, Double)]): (Array[Int], Array[Double]) = {
+    val neighbors_ = dstNeighbors.map { case (dstNeighborId, weight) =>
+      var unnormProb = weight / q
+      if (srcId == dstNeighborId) unnormProb = weight / p
+      else if (srcNeighbors.exists(_._1 == dstNeighborId)) unnormProb = weight
+
+      (dstNeighborId, unnormProb)
+    }
+
+    setupAlias(neighbors_)
+  }
+
+  def drawAlias(J: Array[Int], q: Array[Double]): Int = {
+    val K = J.length
+    val kk = math.floor(math.random * K).toInt
+
+    if (math.random < q(kk)) kk
+    else J(kk)
+  }
+
+  lazy val createUndirectedEdge = (srcId: Long, dstId: Long, weight: Double) => {
+    Array(
+      (srcId, Array((dstId, weight))),
+      (dstId, Array((srcId, weight)))
+    )
+  }
+  
+  lazy val createDirectedEdge = (srcId: Long, dstId: Long, weight: Double) => {
+    Array(
+      (srcId, Array((dstId, weight)))
+    )
+  }
+}
diff --git a/node2vec_spark/src/main/scala/com/navercorp/graph/package.scala b/node2vec_spark/src/main/scala/com/navercorp/graph/package.scala
new file mode 100644
index 0000000..1b83969
--- /dev/null
+++ b/node2vec_spark/src/main/scala/com/navercorp/graph/package.scala
@@ -0,0 +1,12 @@
+package com.navercorp
+
+import java.io.Serializable
+
+package object graph {
+  case class NodeAttr(var neighbors: Array[(Long, Double)] = Array.empty[(Long, Double)],
+                      var path: Array[Long] = Array.empty[Long]) extends Serializable
+
+  case class EdgeAttr(var dstNeighbors: Array[Long] = Array.empty[Long],
+                      var J: Array[Int] = Array.empty[Int],
+                      var q: Array[Double] = Array.empty[Double]) extends Serializable
+}
diff --git a/node2vec_spark/src/main/scala/com/navercorp/lib/AbstractParams.scala b/node2vec_spark/src/main/scala/com/navercorp/lib/AbstractParams.scala
new file mode 100644
index 0000000..0790ab9
--- /dev/null
+++ b/node2vec_spark/src/main/scala/com/navercorp/lib/AbstractParams.scala
@@ -0,0 +1,53 @@
+/*
+ * 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 com.navercorp.lib
+
+import scala.reflect.runtime.universe._
+
+/**
+  * Abstract class for parameter case classes.
+  * This overrides the [[toString]] method to print all case class fields by name and value.
+  * @tparam T  Concrete parameter class.
+  */
+abstract class AbstractParams[T: TypeTag] {
+
+  private def tag: TypeTag[T] = typeTag[T]
+
+  /**
+    * Finds all case class fields in concrete class instance, and outputs them in JSON-style format:
+    * {
+    *   [field name]:\t[field value]\n
+    *   [field name]:\t[field value]\n
+    *   ...
+    * }
+    */
+  override def toString: String = {
+    val tpe = tag.tpe
+    val allAccessors = tpe.declarations.collect {
+      case m: MethodSymbol if m.isCaseAccessor => m
+    }
+    val mirror = runtimeMirror(getClass.getClassLoader)
+    val instanceMirror = mirror.reflect(this)
+    allAccessors.map { f =>
+      val paramName = f.name.toString
+      val fieldMirror = instanceMirror.reflectField(f)
+      val paramValue = fieldMirror.get
+      s"  $paramName:\t$paramValue"
+    }.mkString("{\n", ",\n", "\n}")
+  }
+}
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..faa710c
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,3 @@
+networkx==1.11
+numpy==1.11.2
+gensim==0.13.3
diff --git a/src/main.py b/src/main.py
index 7caa4e5..82ac735 100644
--- a/src/main.py
+++ b/src/main.py
@@ -30,11 +30,11 @@ def parse_args():
 	parser.add_argument('--dimensions', type=int, default=128,
 	                    help='Number of dimensions. Default is 128.')
 
-	parser.add_argument('--walk-length', type=int, default=10,
-	                    help='Length of walk per source. Default is 10.')
+	parser.add_argument('--walk-length', type=int, default=80,
+	                    help='Length of walk per source. Default is 80.')
 
 	parser.add_argument('--num-walks', type=int, default=10,
-	                    help='Number of walks per source. Default is 40.')
+	                    help='Number of walks per source. Default is 10.')
 
 	parser.add_argument('--window-size', type=int, default=10,
                     	help='Context size for optimization. Default is 10.')
@@ -99,9 +99,6 @@ def main(args):
 	walks = G.simulate_walks(args.num_walks, args.walk_length)
 	learn_embeddings(walks)
 
-args = parse_args()
-main(args)
-
-
-
-
+if __name__ == "__main__":
+	args = parse_args()
+	main(args)