diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala
index 102dc2d2dd..5dd6f13235 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala
@@ -140,8 +140,32 @@ object PageRank extends Logging {
    */
   def runWithOptions[VD: ClassTag, ED: ClassTag](
       graph: Graph[VD, ED], numIter: Int, resetProb: Double = 0.15,
-      srcId: Option[VertexId] = None): Graph[Double, Double] =
-  {
+      srcId: Option[VertexId] = None): Graph[Double, Double] = {
+    runWithOptions(graph, numIter, resetProb, srcId, normalized = true)
+  }
+
+  /**
+   * Run PageRank for a fixed number of iterations returning a graph
+   * with vertex attributes containing the PageRank and edge
+   * attributes the normalized edge weight.
+   *
+   * @tparam VD the original vertex attribute (not used)
+   * @tparam ED the original edge attribute (not used)
+   *
+   * @param graph the graph on which to compute PageRank
+   * @param numIter the number of iterations of PageRank to run
+   * @param resetProb the random reset probability (alpha)
+   * @param srcId the source vertex for a Personalized Page Rank (optional)
+   * @param normalized whether or not to normalize rank sum
+   *
+   * @return the graph containing with each vertex containing the PageRank and each edge
+   *         containing the normalized weight.
+   *
+   * @since 3.2.0
+   */
+  def runWithOptions[VD: ClassTag, ED: ClassTag](
+      graph: Graph[VD, ED], numIter: Int, resetProb: Double,
+      srcId: Option[VertexId], normalized: Boolean): Graph[Double, Double] = {
     require(numIter > 0, s"Number of iterations must be greater than 0," +
       s" but got ${numIter}")
     require(resetProb >= 0 && resetProb <= 1, s"Random reset probability must belong" +
@@ -179,8 +203,13 @@ object PageRank extends Logging {
       iteration += 1
     }
 
-    // SPARK-18847 If the graph has sinks (vertices with no outgoing edges) correct the sum of ranks
-    normalizeRankSum(rankGraph, personalized)
+    if (normalized) {
+      // SPARK-18847 If the graph has sinks (vertices with no outgoing edges),
+      // correct the sum of ranks
+      normalizeRankSum(rankGraph, personalized)
+    } else {
+      rankGraph
+    }
   }
 
   /**
@@ -204,6 +233,34 @@ object PageRank extends Logging {
   def runWithOptionsWithPreviousPageRank[VD: ClassTag, ED: ClassTag](
       graph: Graph[VD, ED], numIter: Int, resetProb: Double, srcId: Option[VertexId],
       preRankGraph: Graph[Double, Double]): Graph[Double, Double] = {
+    runWithOptionsWithPreviousPageRank(
+      graph, numIter, resetProb, srcId, normalized = true, preRankGraph
+    )
+  }
+
+  /**
+   * Run PageRank for a fixed number of iterations returning a graph
+   * with vertex attributes containing the PageRank and edge
+   * attributes the normalized edge weight.
+   *
+   * @tparam VD the original vertex attribute (not used)
+   * @tparam ED the original edge attribute (not used)
+   *
+   * @param graph the graph on which to compute PageRank
+   * @param numIter the number of iterations of PageRank to run
+   * @param resetProb the random reset probability (alpha)
+   * @param srcId the source vertex for a Personalized Page Rank (optional)
+   * @param normalized whether or not to normalize rank sum
+   * @param preRankGraph PageRank graph from which to keep iterating
+   *
+   * @return the graph containing with each vertex containing the PageRank and each edge
+   *         containing the normalized weight.
+   *
+   * @since 3.2.0
+   */
+  def runWithOptionsWithPreviousPageRank[VD: ClassTag, ED: ClassTag](
+      graph: Graph[VD, ED], numIter: Int, resetProb: Double, srcId: Option[VertexId],
+      normalized: Boolean, preRankGraph: Graph[Double, Double]): Graph[Double, Double] = {
     require(numIter > 0, s"Number of iterations must be greater than 0," +
       s" but got ${numIter}")
     require(resetProb >= 0 && resetProb <= 1, s"Random reset probability must belong" +
@@ -238,8 +295,13 @@ object PageRank extends Logging {
       iteration += 1
     }
 
-    // SPARK-18847 If the graph has sinks (vertices with no outgoing edges) correct the sum of ranks
-    normalizeRankSum(rankGraph, personalized)
+    if (normalized) {
+      // SPARK-18847 If the graph has sinks (vertices with no outgoing edges),
+      // correct the sum of ranks
+      normalizeRankSum(rankGraph, personalized)
+    } else {
+      rankGraph
+    }
   }
 
   /**
diff --git a/graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala
index 8008a89c6c..caa2fdcdf5 100644
--- a/graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala
+++ b/graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala
@@ -233,7 +233,37 @@ class PageRankSuite extends SparkFunSuite with LocalSparkContext {
       assert(totalIters == 19)
       assert(iterAfterHalfCheckPoint == 18)
     }
-  } // end of Grid PageRank
+  } // end of Grid PageRank with checkpoint
+
+  test("Grid PageRank with checkpoint without intermediate normalization") {
+    withSpark { sc =>
+      // Check that 6 iterations in a row are equivalent
+      // to 3 times 2 iterations without intermediate normalization
+      val rows = 10
+      val cols = 10
+      val resetProb = 0.15
+      val gridGraph = GraphGenerators.gridGraph(sc, rows, cols).cache()
+
+      val ranksA: Array[(VertexId, Double)] = PageRank.runWithOptions(
+        gridGraph, numIter = 6, resetProb, srcId = None, normalized = true
+      ).vertices.collect()
+
+      val preRankGraph1 = PageRank.runWithOptions(
+        gridGraph, numIter = 2, resetProb, srcId = None, normalized = false
+      )
+
+      val preRankGraph2 = PageRank.runWithOptionsWithPreviousPageRank(
+        gridGraph, numIter = 2, resetProb, srcId = None, normalized = false, preRankGraph1
+      )
+
+      val ranksB: Array[(VertexId, Double)] = PageRank.runWithOptionsWithPreviousPageRank(
+        gridGraph, numIter = 2, resetProb, srcId = None, normalized = true, preRankGraph2
+      ).vertices.collect()
+
+      // assert that all scores are equal
+      assert(ranksA.zip(ranksB).forall { case(rankA, rankB) => rankA == rankB })
+    }
+  } // end of Grid PageRank with checkpoint without intermediate normalization
 
   test("Chain PageRank") {
     withSpark { sc =>