# 图算法

GraphX包括一组图算法来简化分析任务。这些算法包含在`org.apache.spark.graphx.lib`包中，可以被直接访问。

## PageRank算法

PageRank度量一个图中每个顶点的重要程度，假定从u到v的一条边代表v的重要性标签。例如，一个Twitter用户被许多其它人粉，该用户排名很高。GraphX带有静态和动态PageRank的实现方法 ，这些方法在PageRank object中。静态的PageRank运行固定次数 的迭代，而动态的PageRank一直运行，直到收敛。GraphOps允许直接调用这些算法作为图上的方法。

GraphX包含一个我们可以运行PageRank的社交网络数据集的例子。用户集在`graphx/data/users.txt`中，用户之间的关系在`graphx/data/followers.txt`中。我们通过下面的方法计算 每个用户的PageRank。

``````// Load the edges as a graph
val graph = GraphLoader.edgeListFile(sc, "graphx/data/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 fields = line.split(",")
(fields(0).toLong, fields(1))
}
val ranksByUsername = users.join(ranks).map {
case (id, (username, rank)) => (username, rank)
}
// Print the result
``````

## 连通体算法

``````/ Load the graph as in the PageRank example
val graph = GraphLoader.edgeListFile(sc, "graphx/data/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 fields = line.split(",")
(fields(0).toLong, fields(1))
}
val ccByUsername = users.join(cc).map {
case (id, (username, cc)) => (username, cc)
}
// Print the result
``````

## 三角形计数算法

``````// 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)
// 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 fields = line.split(",")
(fields(0).toLong, fields(1))
}
val triCountByUsername = users.join(triCounts).map { case (id, (username, tc)) =>