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object
ConnectedComponents
Connected components algorithm.

object
LabelPropagation
Label Propagation algorithm.

object
PageRank extends Logging
PageRank algorithm implementation.
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 toi
andoutDeg[j]
is the out degree of vertexj
. Note
This is not the "normalized" PageRank and as a consequence pages that have no inlinks will have a PageRank of alpha.

object
SVDPlusPlus
Implementation of SVD++ algorithm.

object
ShortestPaths extends Serializable
Computes shortest paths to the given set of landmark vertices, returning a graph where each vertex attribute is a map containing the shortestpath distance to each reachable landmark.

object
StronglyConnectedComponents
Strongly connected components algorithm implementation.

object
TriangleCount
Compute the number of triangles passing through each vertex.
Compute the number of triangles passing through each vertex.
The algorithm is relatively straightforward and can be computed in three steps:
 Compute the set of neighbors for each vertex
 For each edge compute the intersection of the sets and send the count to both vertices.
 Compute the sum at each vertex and divide by two since each triangle is counted twice.
There are two implementations. The default
TriangleCount.run
implementation first removes self cycles and canonicalizes the graph to ensure that the following conditions hold: There are no self edges
 All edges are oriented (src is greater than dst)
 There are no duplicate edges
However, the canonicalization procedure is costly as it requires repartitioning the graph. If the input data is already in "canonical form" with self cycles removed then the
TriangleCount.runPreCanonicalized
should be used instead.val canonicalGraph = graph.mapEdges(e => 1).removeSelfEdges().canonicalizeEdges() val counts = TriangleCount.runPreCanonicalized(canonicalGraph).vertices