Algorithms

Now we dive into the most interesting part of GraphX: algorithms and the graph parallel computation APIs to implement more algorithms. The following table shows a bird's eye view of the algorithms:

Type	GraphX method/example
Graph-Parallel Computation	The method is aggregateMessages(), Function Pregel(). Refer to https://issues.apache.org/jira/browse/SPARK-5062 for examples.
PageRank	The method is PageRank(). As an example, refer to the influential papers in a citation network, Influencer in retweet. You can specifically check out the following: staticPageRank(): This provides a static no of iterations and dynamic tolerance; see the parameters (tol versus numIter) personalizedPageRank(): This is a variation of PageRank that gives a rank relative to a specified "source" vertex in the graph-People
You May Know ShortestPaths and SVD++	The methods are ShortestPaths() and SVD++. As an example, consider the fact that SDV++ takes an RDD of edges.
LabelPropagation...