diff options
| -rw-r--r-- | data/graphx/followers.txt (renamed from graphx/data/followers.txt) | 0 | ||||
| -rw-r--r-- | data/graphx/users.txt (renamed from graphx/data/users.txt) | 0 | ||||
| -rw-r--r-- | docs/graphx-programming-guide.md | 18 |
3 files changed, 9 insertions, 9 deletions
diff --git a/graphx/data/followers.txt b/data/graphx/followers.txt index 7bb8e900e2..7bb8e900e2 100644 --- a/graphx/data/followers.txt +++ b/data/graphx/followers.txt diff --git a/graphx/data/users.txt b/data/graphx/users.txt index 982d19d50b..982d19d50b 100644 --- a/graphx/data/users.txt +++ b/data/graphx/users.txt diff --git a/docs/graphx-programming-guide.md b/docs/graphx-programming-guide.md index 81cf17475f..e376b6638e 100644 --- a/docs/graphx-programming-guide.md +++ b/docs/graphx-programming-guide.md @@ -1007,15 +1007,15 @@ PageRank measures the importance of each vertex in a graph, assuming an edge fro GraphX comes with static and dynamic implementations of PageRank as methods on the [`PageRank` object][PageRank]. Static PageRank runs for a fixed number of iterations, while dynamic PageRank runs until the ranks converge (i.e., stop changing by more than a specified tolerance). [`GraphOps`][GraphOps] allows calling these algorithms directly as methods on `Graph`. -GraphX also 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 compute the PageRank of each user as follows: +GraphX also includes an example social network dataset that we can run PageRank on. A set of users is given in `data/graphx/users.txt`, and a set of relationships between users is given in `data/graphx/followers.txt`. We compute the PageRank of each user as follows: {% highlight scala %} // Load the edges as a graph -val graph = GraphLoader.edgeListFile(sc, "graphx/data/followers.txt") +val graph = GraphLoader.edgeListFile(sc, "data/graphx/followers.txt") // Run PageRank val ranks = graph.pageRank(0.0001).vertices // Join the ranks with the usernames -val users = sc.textFile("graphx/data/users.txt").map { line => +val users = sc.textFile("data/graphx/users.txt").map { line => val fields = line.split(",") (fields(0).toLong, fields(1)) } @@ -1032,11 +1032,11 @@ The connected components algorithm labels each connected component of the graph {% highlight scala %} // Load the graph as in the PageRank example -val graph = GraphLoader.edgeListFile(sc, "graphx/data/followers.txt") +val graph = GraphLoader.edgeListFile(sc, "data/graphx/followers.txt") // Find the connected components val cc = graph.connectedComponents().vertices // Join the connected components with the usernames -val users = sc.textFile("graphx/data/users.txt").map { line => +val users = sc.textFile("data/graphx/users.txt").map { line => val fields = line.split(",") (fields(0).toLong, fields(1)) } @@ -1053,11 +1053,11 @@ A vertex is part of a triangle when it has two adjacent vertices with an edge be {% highlight scala %} // Load the edges in canonical order and partition the graph for triangle count -val graph = GraphLoader.edgeListFile(sc, "graphx/data/followers.txt", true).partitionBy(PartitionStrategy.RandomVertexCut) +val graph = GraphLoader.edgeListFile(sc, "data/graphx/followers.txt", true).partitionBy(PartitionStrategy.RandomVertexCut) // Find the triangle count for each vertex val triCounts = graph.triangleCount().vertices // Join the triangle counts with the usernames -val users = sc.textFile("graphx/data/users.txt").map { line => +val users = sc.textFile("data/graphx/users.txt").map { line => val fields = line.split(",") (fields(0).toLong, fields(1)) } @@ -1081,11 +1081,11 @@ all of this in just a few lines with GraphX: val sc = new SparkContext("spark://master.amplab.org", "research") // Load my user data and parse into tuples of user id and attribute list -val users = (sc.textFile("graphx/data/users.txt") +val users = (sc.textFile("data/graphx/users.txt") .map(line => line.split(",")).map( parts => (parts.head.toLong, parts.tail) )) // Parse the edge data which is already in userId -> userId format -val followerGraph = GraphLoader.edgeListFile(sc, "graphx/data/followers.txt") +val followerGraph = GraphLoader.edgeListFile(sc, "data/graphx/followers.txt") // Attach the user attributes val graph = followerGraph.outerJoinVertices(users) { |