From 5e35d39e0f26db3b669bc2318bd7b3f9f6c5fc50 Mon Sep 17 00:00:00 2001
From: Ankur Dave <ankurdave@gmail.com>
Date: Sun, 12 Jan 2014 13:10:53 -0800
Subject: [PATCH] Add PageRank example and data

---
 docs/graphx-programming-guide.md              | 32 ++++++++++++++++++-
 graphx/data/followers.txt                     | 12 +++++++
 graphx/data/users.txt                         |  6 ++++
 .../apache/spark/graphx/lib/PageRank.scala    |  2 +-
 4 files changed, 50 insertions(+), 2 deletions(-)
 create mode 100644 graphx/data/followers.txt
 create mode 100644 graphx/data/users.txt

diff --git a/docs/graphx-programming-guide.md b/docs/graphx-programming-guide.md
index 7f93754edb..52668b07c8 100644
--- a/docs/graphx-programming-guide.md
+++ b/docs/graphx-programming-guide.md
@@ -470,10 +470,40 @@ things to worry about.)
 # Graph Algorithms
 <a name="graph_algorithms"></a>
 
-This section should describe the various algorithms and how they are used.
+GraphX includes a set of graph algorithms in to simplify analytics. The algorithms are contained in the `org.apache.spark.graphx.lib` package and can be accessed directly as methods on `Graph` via an implicit conversion to [`Algorithms`][Algorithms]. This section describes the algorithms and how they are used.
+
+[Algorithms]: api/graphx/index.html#org.apache.spark.graphx.lib.Algorithms
 
 ## PageRank
 
+PageRank measures the importance of each vertex in a graph, assuming an edge from *u* to *v* represents an endorsement of *v*'s importance by *u*. For example, if a Twitter user is followed by many others, the user will be ranked highly.
+
+Spark includes an example social network dataset that we can run PageRank on. A set of users is given in `graphx/data/users.txt`, and a set of relationships between users is given in `graphx/data/followers.txt`. We can compute the PageRank of each user as follows:
+
+{% highlight scala %}
+// Load the implicit conversion to Algorithms
+import org.apache.spark.graphx.lib._
+// Load the datasets into a graph
+val users = sc.textFile("graphx/data/users.txt").map { line =>
+  val fields = line.split("\\s+")
+  (fields(0).toLong, fields(1))
+}
+val followers = sc.textFile("graphx/data/followers.txt").map { line =>
+  val fields = line.split("\\s+")
+  Edge(fields(0).toLong, fields(1).toLong, 1)
+}
+val graph = Graph(users, followers)
+// Run PageRank
+val ranks = graph.pageRank(0.0001).vertices
+// Join the ranks with the usernames
+val ranksByUsername = users.leftOuterJoin(ranks).map {
+  case (id, (username, rankOpt)) => (username, rankOpt.getOrElse(0.0))
+}
+// Print the result
+println(ranksByUsername.collect().mkString("\n"))
+{% endhighlight %}
+
+
 ## Connected Components
 
 ## Shortest Path
diff --git a/graphx/data/followers.txt b/graphx/data/followers.txt
new file mode 100644
index 0000000000..0f46d80806
--- /dev/null
+++ b/graphx/data/followers.txt
@@ -0,0 +1,12 @@
+2 1
+3 1
+4 1
+6 1
+3 2
+6 2
+7 2
+6 3
+7 3
+7 6
+6 7
+3 7
diff --git a/graphx/data/users.txt b/graphx/data/users.txt
new file mode 100644
index 0000000000..ce3d06c600
--- /dev/null
+++ b/graphx/data/users.txt
@@ -0,0 +1,6 @@
+1 BarackObama
+2 ericschmidt
+3 jeresig
+4 justinbieber
+6 matei_zaharia
+7 odersky
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 809b6d0855..cf95267e77 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
@@ -106,7 +106,7 @@ object PageRank extends Logging {
    * @tparam ED the original edge attribute (not used)
    *
    * @param graph the graph on which to compute PageRank
-   * @param tol the tolerance allowed at convergence (smaller => more * accurate).
+   * @param tol the tolerance allowed at convergence (smaller => more accurate).
    * @param resetProb the random reset probability (alpha)
    *
    * @return the graph containing with each vertex containing the PageRank and each edge