> For the complete documentation index, see [llms.txt](https://george-jen.gitbook.io/data-science-and-apache-spark/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://george-jen.gitbook.io/data-science-and-apache-spark/class-pagerank.md).

# Class PageRank

Object org.apache.spark.graphx.lib.PageRank

PageRank algorithm implementation. There are two implementations of PageRank implemented.

The first implementation uses the standalone Graph interface and runs PageRank for a fixed number of iterations:

```
 var PR = Array.fill(n)( 1.0 )
 val oldPR = Array.fill(n)( 1.0 )
 for( iter <- 0 until numIter ) {
   swap(oldPR, PR)
   for( i <- 0 until n ) {
     PR[i] = alpha + (1 - alpha) * inNbrs[i].map(j => oldPR[j] / outDeg[j]).sum
   }
 }
```

The second implementation uses the Pregel interface and runs PageRank until convergence:

```
 var PR = Array.fill(n)( 1.0 )
 val oldPR = Array.fill(n)( 0.0 )
 while( max(abs(PR - oldPr)) > tol ) {
   swap(oldPR, PR)
   for( i <- 0 until n if abs(PR[i] - oldPR[i]) > tol ) {
     PR[i] = alpha + (1 - \alpha) * inNbrs[i].map(j => oldPR[j] / outDeg[j]).sum
   }
 }

```

alpha is the random reset probability (typically 0.15), inNbrs\[i] is the set of neighbors which link to i and outDeg\[j] is the out degree of vertex j.

Note: This is not the "normalized" PageRank and as a consequence pages that have no inlinks will have a PageRank of alpha.

Methods:

```
static <VD,ED> Graph<Object,Object>	run(Graph<VD,ED> graph, int numIter, double resetProb, scala.reflect.ClassTag<VD> evidence$1, scala.reflect.ClassTag<ED> evidence$2)

Run PageRank for a fixed number of iterations returning a graph with vertex 
attributes containing the PageRank and edge attributes the normalized edge weight.
static <VD,ED> Graph<Vector,Object>	runParallelPersonalizedPageRank(Graph<VD,ED> graph, int numIter, double resetProb, long[] sources, scala.reflect.ClassTag<VD> evidence$5, scala.reflect.ClassTag<ED> evidence$6)

Run Personalized PageRank for a fixed number of iterations, for a set of starting nodes in parallel.

static <VD,ED> Graph<Object,Object>	runUntilConvergence(Graph<VD,ED> graph, double tol, double resetProb, scala.reflect.ClassTag<VD> evidence$7, scala.reflect.ClassTag<ED> evidence$8)

Run a dynamic version of PageRank returning a graph with vertex attributes containing the PageRank and edge attributes containing the normalized edge weight.

static <VD,ED> Graph<Object,Object>	runUntilConvergenceWithOptions(Graph<VD,ED> graph, double tol, double resetProb, scala.Option<Object> srcId, scala.reflect.ClassTag<VD> evidence$9, scala.reflect.ClassTag<ED> evidence$10)

Run a dynamic version of PageRank returning a graph with vertex attributes containing the PageRank and edge attributes containing the normalized edge weight.

static <VD,ED> Graph<Object,Object>	runWithOptions(Graph<VD,ED> graph, int numIter, double resetProb, scala.Option<Object> srcId, scala.reflect.ClassTag<VD> evidence$3, scala.reflect.ClassTag<ED> evidence$4)

Run PageRank for a fixed number of iterations returning a graph with vertex attributes containing the PageRank and edge attributes the normalized edge weight.

```


---

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