diff --git a/graphx/src/main/scala/org/apache/spark/graphx/util/GraphGenerators.scala b/graphx/src/main/scala/org/apache/spark/graphx/util/GraphGenerators.scala
index 419731146d..ea531b6cf0 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/util/GraphGenerators.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/util/GraphGenerators.scala
@@ -87,11 +87,10 @@ object GraphGenerators extends Logging {
   }
 
   /**
-   * Randomly samples from a log normal distribution whose corresponding normal distribution has
-   * the given mean and standard deviation. It uses the formula `X = exp(m+s*Z)` where `m`,
-   * `s` are the mean, standard deviation of the lognormal distribution and
-   * `Z ~ N(0, 1)`. In this function,
-   * `m = e^(mu+sigma^2/2)` and `s = sqrt[(e^(sigma^2) - 1)(e^(2*mu+sigma^2))]`.
+   * Randomly samples from the given mean and standard deviation of the normal distribution.
+   * It uses the formula `X = exp(mu+sigma*Z)` where `mu`,
+   * `sigma` are the mean, standard deviation of the normal distribution and
+   * `Z ~ N(0, 1)`.
    *
    * @param mu the mean of the normal distribution
    * @param sigma the standard deviation of the normal distribution
@@ -102,10 +101,6 @@ object GraphGenerators extends Logging {
       mu: Double, sigma: Double, maxVal: Int, seed: Long = -1): Int = {
     val rand = if (seed == -1) new Random() else new Random(seed)
 
-    val sigmaSq = sigma * sigma
-    val m = math.exp(mu + sigmaSq / 2.0)
-    // expm1 is exp(m)-1 with better accuracy for tiny m
-    val s = math.sqrt(math.expm1(sigmaSq) * math.exp(2*mu + sigmaSq))
     // Z ~ N(0, 1)
     var X: Double = maxVal