Reindenting documentation.
This commit is contained in:
Joseph E. Gonzalez
parent
d316cad9b1
commit
15958ca65a
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@ -6,19 +6,20 @@ import org.apache.spark._
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/**
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* The Analytics object contains a collection of basic graph analytics
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* algorithms that operate largely on the graph structure.
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* algorithms that operate largely on the graph structure.
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*
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* In addition the Analytics object contains a driver `main` which can
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* be used to apply the various functions to graphs in standard formats.
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* In addition the Analytics object contains a driver `main` which can
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* be used to apply the various functions to graphs in standard
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* formats.
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*/
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object Analytics extends Logging {
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/**
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* Run PageRank for a fixed number of iterations returning a graph
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* with vertex attributes containing the PageRank and edge attributes
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* the normalized edge weight.
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* Run PageRank for a fixed number of iterations returning a graph
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* with vertex attributes containing the PageRank and edge
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* attributes the normalized edge weight.
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*
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* The following PageRank fixed point is computed for each vertex.
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* The following PageRank fixed point is computed for each vertex.
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*
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* {{{
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* var PR = Array.fill(n)( 1.0 )
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@ -31,12 +32,13 @@ object Analytics extends Logging {
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* }
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* }}}
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*
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* where `alpha` is the random reset probability (typically 0.15),
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* `inNbrs[i]` is the set of neighbors whick link to `i` and `outDeg[j]`
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* is the out degree of vertex `j`.
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* where `alpha` is the random reset probability (typically 0.15),
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* `inNbrs[i]` is the set of neighbors whick link to `i` and
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* `outDeg[j]` is the out degree of vertex `j`.
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*
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* Note that this is not the "normalized" PageRank and as a consequence
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* pages that have no inlinks will have a PageRank of alpha.
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* Note that this is not the "normalized" PageRank and as a
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* consequence pages that have no inlinks will have a PageRank of
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* alpha.
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*
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* @tparam VD the original vertex attribute (not used)
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* @tparam ED the original edge attribute (not used)
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@ -45,8 +47,8 @@ object Analytics extends Logging {
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* @param numIter the number of iterations of PageRank to run
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* @param resetProb the random reset probability (alpha)
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*
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* @return the graph containing with each vertex containing the PageRank and
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* each edge containing the normalized weight.
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* @return the graph containing with each vertex containing the
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* PageRank and each edge containing the normalized weight.
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*
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*/
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def pagerank[VD: Manifest, ED: Manifest](
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@ -54,8 +56,8 @@ object Analytics extends Logging {
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Graph[Double, Double] = {
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/**
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* Initialize the pagerankGraph with each edge attribute
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* having weight 1/outDegree and each vertex with attribute 1.0.
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* Initialize the pagerankGraph with each edge attribute having
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* weight 1/outDegree and each vertex with attribute 1.0.
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*/
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val pagerankGraph: Graph[Double, Double] = graph
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// Associate the degree with each vertex
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@ -85,10 +87,11 @@ object Analytics extends Logging {
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vertexProgram, sendMessage, messageCombiner)
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}
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/**
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* Run a dynamic version of PageRank returning a graph with vertex attributes
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* containing the PageRank and edge attributes containing the normalized
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* edge weight.
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* Run a dynamic version of PageRank returning a graph with vertex
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* attributes containing the PageRank and edge attributes containing
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* the normalized edge weight.
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*
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* {{{
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* var PR = Array.fill(n)( 1.0 )
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@ -101,22 +104,24 @@ object Analytics extends Logging {
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* }
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* }}}
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*
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* where `alpha` is the random reset probability (typically 0.15),
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* `inNbrs[i]` is the set of neighbors whick link to `i` and `outDeg[j]`
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* is the out degree of vertex `j`.
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* where `alpha` is the random reset probability (typically 0.15),
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* `inNbrs[i]` is the set of neighbors whick link to `i` and
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* `outDeg[j]` is the out degree of vertex `j`.
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*
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* Note that this is not the "normalized" PageRank and as a consequence
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* pages that have no inlinks will have a PageRank of alpha.
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* Note that this is not the "normalized" PageRank and as a
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* consequence pages that have no inlinks will have a PageRank of
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* alpha.
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*
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* @tparam VD the original vertex attribute (not used)
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* @tparam ED the original edge attribute (not used)
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*
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* @param graph the graph on which to compute PageRank
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* @param tol the tolerance allowed at convergence (smaller => more accurate).
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* @param tol the tolerance allowed at convergence (smaller => more
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* accurate).
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* @param resetProb the random reset probability (alpha)
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*
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* @return the graph containing with each vertex containing the PageRank and
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* each edge containing the normalized weight.
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* @return the graph containing with each vertex containing the
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* PageRank and each edge containing the normalized weight.
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*/
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def deltaPagerank[VD: Manifest, ED: Manifest](
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graph: Graph[VD, ED], tol: Double, resetProb: Double = 0.15):
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@ -163,18 +168,19 @@ object Analytics extends Logging {
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/**
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* Compute the connected component membership of each vertex
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* and return an RDD with the vertex value containing the
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* lowest vertex id in the connected component containing
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* that vertex.
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* Compute the connected component membership of each vertex and
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* return an RDD with the vertex value containing the lowest vertex
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* id in the connected component containing that vertex.
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*
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* @tparam VD the vertex attribute type (discarded in the computation)
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* @tparam VD the vertex attribute type (discarded in the
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* computation)
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* @tparam ED the edge attribute type (preserved in the computation)
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*
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* @param graph the graph for which to compute the connected components
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* @param graph the graph for which to compute the connected
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* components
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*
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* @return a graph with vertex attributes containing the smallest vertex
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* in each connected component
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* @return a graph with vertex attributes containing the smallest
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* vertex in each connected component
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*/
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def connectedComponents[VD: Manifest, ED: Manifest](graph: Graph[VD, ED]):
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Graph[Vid, ED] = {
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@ -8,8 +8,17 @@ package org.apache.spark.graph
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* @tparam ED type of the edge attribute
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*/
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case class Edge[@specialized(Char, Int, Boolean, Byte, Long, Float, Double) ED] (
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/**
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* The vertex id of the source vertex
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*/
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var srcId: Vid = 0,
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/**
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* The vertex id of the target vertex.
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*/
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var dstId: Vid = 0,
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/**
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* The attribute associated with the edge.
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*/
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var attr: ED = nullValue[ED]) {
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/**
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@ -23,10 +32,12 @@ case class Edge[@specialized(Char, Int, Boolean, Byte, Long, Float, Double) ED]
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/**
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* Return the relative direction of the edge to the corresponding vertex.
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* Return the relative direction of the edge to the corresponding
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* vertex.
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*
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* @param vid the id of one of the two vertices in the edge.
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* @return the relative direction of the edge to the corresponding vertex.
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* @return the relative direction of the edge to the corresponding
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* vertex.
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*/
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def relativeDirection(vid: Vid): EdgeDirection =
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if (vid == srcId) EdgeDirection.Out else { assert(vid == dstId); EdgeDirection.In }
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@ -1,14 +1,15 @@
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package org.apache.spark.graph
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/**
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* An edge triplet represents two vertices and edge along with their attributes.
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* An edge triplet represents two vertices and edge along with their
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* attributes.
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*
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* @tparam VD the type of the vertex attribute.
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* @tparam ED the type of the edge attribute
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*
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* @todo specialize edge triplet for basic types, though when I last tried
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* specializing I got a warning about inherenting from a type that is not
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* a trait.
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* @todo specialize edge triplet for basic types, though when I last
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* tried specializing I got a warning about inherenting from a type
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* that is not a trait.
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*/
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class EdgeTriplet[VD, ED] extends Edge[ED] {
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// class EdgeTriplet[@specialized(Char, Int, Boolean, Byte, Long, Float, Double) VD: ClassManifest,
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@ -7,9 +7,10 @@ import org.apache.spark.util.ClosureCleaner
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/**
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* `GraphOps` contains additional functionality (syntatic sugar) for the graph
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* type and is implicitly constructed for each Graph object. All operations in
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* `GraphOps` are expressed in terms of the efficient GraphX API.
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* `GraphOps` contains additional functionality (syntatic sugar) for
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* the graph type and is implicitly constructed for each Graph object.
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* All operations in `GraphOps` are expressed in terms of the
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* efficient GraphX API.
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*
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* @tparam VD the vertex attribute type
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* @tparam ED the edge attribute type
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@ -30,7 +31,8 @@ class GraphOps[VD: ClassManifest, ED: ClassManifest](graph: Graph[VD, ED]) {
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/**
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* Compute the in-degree of each vertex in the Graph returning an RDD.
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* Compute the in-degree of each vertex in the Graph returning an
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* RDD.
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* @note Vertices with no in edges are not returned in the resulting RDD.
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*/
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lazy val inDegrees: VertexSetRDD[Int] = degreesRDD(EdgeDirection.In)
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@ -44,8 +46,9 @@ class GraphOps[VD: ClassManifest, ED: ClassManifest](graph: Graph[VD, ED]) {
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/**
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* Compute the degrees of each vertex in the Graph returning an RDD.
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* @note Vertices with no edges are not returned in the resulting RDD.
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* Compute the degrees of each vertex in the Graph returning an RDD.
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* @note Vertices with no edges are not returned in the resulting
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* RDD.
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*/
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lazy val degrees: VertexSetRDD[Int] = degreesRDD(EdgeDirection.Both)
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@ -53,8 +56,8 @@ class GraphOps[VD: ClassManifest, ED: ClassManifest](graph: Graph[VD, ED]) {
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/**
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* Compute the neighboring vertex degrees.
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*
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* @param edgeDirection the direction along which to collect neighboring
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* vertex attributes.
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* @param edgeDirection the direction along which to collect
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* neighboring vertex attributes.
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*/
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private def degreesRDD(edgeDirection: EdgeDirection): VertexSetRDD[Int] = {
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graph.aggregateNeighbors((vid, edge) => Some(1), _+_, edgeDirection)
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@ -62,29 +65,31 @@ class GraphOps[VD: ClassManifest, ED: ClassManifest](graph: Graph[VD, ED]) {
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/**
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* This function is used to compute a statistic for the neighborhood of each
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* vertex and returns a value for all vertices (including those without
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* neighbors).
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* This function is used to compute a statistic for the neighborhood
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* of each vertex and returns a value for all vertices (including
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* those without neighbors).
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*
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* @note Because the a default value is provided all vertices will have a
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* corresponding entry in the returned RDD.
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* @note Because the a default value is provided all vertices will
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* have a corresponding entry in the returned RDD.
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*
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* @param mapFunc the function applied to each edge adjacent to each vertex.
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* The mapFunc can optionally return None in which case it does not
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* contribute to the final sum.
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* @param reduceFunc the function used to merge the results of each map
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* operation.
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* @param default the default value to use for each vertex if it has no
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* neighbors or the map function repeatedly evaluates to none
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* @param direction the direction of edges to consider (e.g., In, Out, Both).
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* @param mapFunc the function applied to each edge adjacent to each
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* vertex. The mapFunc can optionally return None in which case it
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* does not contribute to the final sum.
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* @param reduceFunc the function used to merge the results of each
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* map operation.
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* @param default the default value to use for each vertex if it has
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* no neighbors or the map function repeatedly evaluates to none
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* @param direction the direction of edges to consider (e.g., In,
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* Out, Both).
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* @tparam VD2 The returned type of the aggregation operation.
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*
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* @return A Spark.RDD containing tuples of vertex identifiers and
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* their resulting value. There will be exactly one entry for ever vertex in
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* the original graph.
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* their resulting value. There will be exactly one entry for ever
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* vertex in the original graph.
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*
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* @example We can use this function to compute the average follower age
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* for each user
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* @example We can use this function to compute the average follower
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* age for each user
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*
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* {{{
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* val graph: Graph[Int,Int] = loadGraph()
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* val averageFollowerAge: RDD[(Int, Int)] =
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@ -138,7 +143,7 @@ class GraphOps[VD: ClassManifest, ED: ClassManifest](graph: Graph[VD, ED]) {
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/**
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* Return the Ids of the neighboring vertices.
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*
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* @param edgeDirection the direction along which to collect
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* @param edgeDirection the direction along which to collect
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* neighboring vertices
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*
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* @return the vertex set of neighboring ids for each vertex.
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* graphs where high degree vertices may force a large ammount of
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* information to be collected to a single location.
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*
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* @param edgeDirection the direction along which to collect
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* @param edgeDirection the direction along which to collect
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* neighboring vertices
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*
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* @return the vertex set of neighboring vertex attributes
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* for each vertex.
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* @return the vertex set of neighboring vertex attributes for each
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* vertex.
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*/
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def collectNeighbors(edgeDirection: EdgeDirection) :
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VertexSetRDD[ Array[(Vid, VD)] ] = {
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@ -186,24 +191,26 @@ class GraphOps[VD: ClassManifest, ED: ClassManifest](graph: Graph[VD, ED]) {
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/**
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* Join the vertices with an RDD and then apply a function from the the
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* vertex and RDD entry to a new vertex value. The input table should
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* contain at most one entry for each vertex. If no entry is provided the
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* map function is skipped and the old value is used.
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* Join the vertices with an RDD and then apply a function from the
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* the vertex and RDD entry to a new vertex value. The input table
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* should contain at most one entry for each vertex. If no entry is
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* provided the map function is skipped and the old value is used.
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*
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* @tparam U the type of entry in the table of updates
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* @param table the table to join with the vertices in the graph. The table
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* should contain at most one entry for each vertex.
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* @param mapFunc the function used to compute the new vertex values. The
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* map function is invoked only for vertices with a corresponding entry in
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* the table otherwise the old vertex value is used.
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* @param table the table to join with the vertices in the graph.
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* The table should contain at most one entry for each vertex.
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* @param mapFunc the function used to compute the new vertex
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* values. The map function is invoked only for vertices with a
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* corresponding entry in the table otherwise the old vertex value
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* is used.
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*
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* @note for small tables this function can be much more efficient than
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* leftJoinVertices
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* @note for small tables this function can be much more efficient
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* than leftJoinVertices
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*
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* @example This function is used to update the vertices with new
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* values based on external data. For example we could add the out
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* degree to each vertex record
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*
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* @example This function is used to update the vertices with new values
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* based on external data. For example we could add the out degree to each
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* vertex record
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* {{{
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* val rawGraph: Graph[Int,()] = Graph.textFile("webgraph")
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* .mapVertices(v => 0)
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@ -5,18 +5,17 @@ import org.apache.spark.rdd.RDD
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/**
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* This object implements a Pregel-like bulk-synchronous
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* message-passing API. However, unlike the original Pregel API
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* the GraphX pregel API factors the sendMessage computation over
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* edges, enables the message sending computation to read both
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* vertex attributes, and finally contrains messages to the graph
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* structure. These changes allow for substantially more efficient
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* distributed execution while also exposing greater flexibility
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* for graph based computation.
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* message-passing API. However, unlike the original Pregel API the
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* GraphX pregel API factors the sendMessage computation over edges,
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* enables the message sending computation to read both vertex
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* attributes, and finally contrains messages to the graph structure.
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* These changes allow for substantially more efficient distributed
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* execution while also exposing greater flexibility for graph based
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* computation.
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*
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* This object present several variants of the bulk synchronous
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* execution that differ only in the edge direction along which
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* messages are sent and whether a fixed number of iterations
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* is used.
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* messages are sent and whether a fixed number of iterations is used.
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*
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* @example We can use the Pregel abstraction to implement PageRank
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* {{{
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@ -46,18 +45,18 @@ object Pregel {
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/**
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* Execute a Pregel-like iterative vertex-parallel abstraction.
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* The user-defined vertex-program `vprog` is executed in parallel
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* on each vertex receiving any inbound messages and computing a new
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* Execute a Pregel-like iterative vertex-parallel abstraction. The
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* user-defined vertex-program `vprog` is executed in parallel on
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* each vertex receiving any inbound messages and computing a new
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* value for the vertex. The `sendMsg` function is then invoked on
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* all out-edges and is used to compute an optional message to the
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* destination vertex. The `mergeMsg` function is a commutative
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* all out-edges and is used to compute an optional message to the
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* destination vertex. The `mergeMsg` function is a commutative
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* associative function used to combine messages destined to the
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* same vertex.
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* same vertex.
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*
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* On the first iteration all vertices receive the `initialMsg` and
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* on subsequent iterations if a vertex does not receive a message then
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* the vertex-program is not invoked.
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* on subsequent iterations if a vertex does not receive a message
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* then the vertex-program is not invoked.
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*
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* This function iterates a fixed number (`numIter`) of iterations.
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*
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@ -67,24 +66,26 @@ object Pregel {
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*
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* @param graph the input graph.
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*
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* @param initialMsg the message each vertex will receive at the
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* on the first iteration.
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* @param initialMsg the message each vertex will receive at the on
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* the first iteration.
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*
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* @param numIter the number of iterations to run this computation.
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*
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* @param vprog the user-defined vertex program which runs on each vertex
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* and receives the inbound message and computes a new vertex value.
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* On the first iteration the vertex program is invoked on all vertices
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* and is passed the default message. On subsequent iterations the
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||||
* vertex program is only invoked on those vertices that receive messages.
|
||||
* @param vprog the user-defined vertex program which runs on each
|
||||
* vertex and receives the inbound message and computes a new vertex
|
||||
* value. On the first iteration the vertex program is invoked on
|
||||
* all vertices and is passed the default message. On subsequent
|
||||
* iterations the vertex program is only invoked on those vertices
|
||||
* that receive messages.
|
||||
*
|
||||
* @param sendMsg a user supplied function that is applied to out edges
|
||||
* of vertices that received messages in the current iteration.
|
||||
* @param sendMsg a user supplied function that is applied to out
|
||||
* edges of vertices that received messages in the current
|
||||
* iteration.
|
||||
*
|
||||
* @param mergeMsg a user supplied function that takes two incoming messages
|
||||
* of type A and merges them into a single message of type A.
|
||||
* ''This function must be commutative and associative and ideally the
|
||||
* size of A should not increase.''
|
||||
* @param mergeMsg a user supplied function that takes two incoming
|
||||
* messages of type A and merges them into a single message of type
|
||||
* A. ''This function must be commutative and associative and
|
||||
* ideally the size of A should not increase.''
|
||||
*
|
||||
* @return the resulting graph at the end of the computation
|
||||
*
|
||||
|
|
@ -100,18 +101,18 @@ object Pregel {
|
|||
|
||||
|
||||
/**
|
||||
* Execute a Pregel-like iterative vertex-parallel abstraction.
|
||||
* The user-defined vertex-program `vprog` is executed in parallel
|
||||
* on each vertex receiving any inbound messages and computing a new
|
||||
* Execute a Pregel-like iterative vertex-parallel abstraction. The
|
||||
* user-defined vertex-program `vprog` is executed in parallel on
|
||||
* each vertex receiving any inbound messages and computing a new
|
||||
* value for the vertex. The `sendMsg` function is then invoked on
|
||||
* all out-edges and is used to compute an optional message to the
|
||||
* destination vertex. The `mergeMsg` function is a commutative
|
||||
* all out-edges and is used to compute an optional message to the
|
||||
* destination vertex. The `mergeMsg` function is a commutative
|
||||
* associative function used to combine messages destined to the
|
||||
* same vertex.
|
||||
* same vertex.
|
||||
*
|
||||
* On the first iteration all vertices receive the `initialMsg` and
|
||||
* on subsequent iterations if a vertex does not receive a message then
|
||||
* the vertex-program is not invoked.
|
||||
* on subsequent iterations if a vertex does not receive a message
|
||||
* then the vertex-program is not invoked.
|
||||
*
|
||||
* This function iterates a fixed number (`numIter`) of iterations.
|
||||
*
|
||||
|
|
@ -121,28 +122,29 @@ object Pregel {
|
|||
*
|
||||
* @param graph the input graph.
|
||||
*
|
||||
* @param initialMsg the message each vertex will receive at the
|
||||
* on the first iteration.
|
||||
* @param initialMsg the message each vertex will receive at the on
|
||||
* the first iteration.
|
||||
*
|
||||
* @param numIter the number of iterations to run this computation.
|
||||
*
|
||||
* @param sendDir the edge direction along which the `sendMsg` function
|
||||
* is invoked.
|
||||
* @param sendDir the edge direction along which the `sendMsg`
|
||||
* function is invoked.
|
||||
*
|
||||
* @param vprog the user-defined vertex program which runs on each vertex
|
||||
* and receives the inbound message and computes a new vertex value.
|
||||
* On the first iteration the vertex program is invoked on all vertices
|
||||
* and is passed the default message. On subsequent iterations the
|
||||
* vertex program is only invoked on those vertices that receive messages.
|
||||
* @param vprog the user-defined vertex program which runs on each
|
||||
* vertex and receives the inbound message and computes a new vertex
|
||||
* value. On the first iteration the vertex program is invoked on
|
||||
* all vertices and is passed the default message. On subsequent
|
||||
* iterations the vertex program is only invoked on those vertices
|
||||
* that receive messages.
|
||||
*
|
||||
* @param sendMsg a user supplied function that is applied to each edge
|
||||
* in the direction `sendDir` adjacent to vertices that received messages
|
||||
* in the current iteration.
|
||||
* @param sendMsg a user supplied function that is applied to each
|
||||
* edge in the direction `sendDir` adjacent to vertices that
|
||||
* received messages in the current iteration.
|
||||
*
|
||||
* @param mergeMsg a user supplied function that takes two incoming messages
|
||||
* of type A and merges them into a single message of type A.
|
||||
* ''This function must be commutative and associative and ideally the
|
||||
* size of A should not increase.''
|
||||
* @param mergeMsg a user supplied function that takes two incoming
|
||||
* messages of type A and merges them into a single message of type
|
||||
* A. ''This function must be commutative and associative and
|
||||
* ideally the size of A should not increase.''
|
||||
*
|
||||
* @return the resulting graph at the end of the computation
|
||||
*
|
||||
|
|
@ -174,18 +176,18 @@ object Pregel {
|
|||
|
||||
|
||||
/**
|
||||
* Execute a Pregel-like iterative vertex-parallel abstraction.
|
||||
* The user-defined vertex-program `vprog` is executed in parallel
|
||||
* on each vertex receiving any inbound messages and computing a new
|
||||
* Execute a Pregel-like iterative vertex-parallel abstraction. The
|
||||
* user-defined vertex-program `vprog` is executed in parallel on
|
||||
* each vertex receiving any inbound messages and computing a new
|
||||
* value for the vertex. The `sendMsg` function is then invoked on
|
||||
* all out-edges and is used to compute an optional message to the
|
||||
* destination vertex. The `mergeMsg` function is a commutative
|
||||
* all out-edges and is used to compute an optional message to the
|
||||
* destination vertex. The `mergeMsg` function is a commutative
|
||||
* associative function used to combine messages destined to the
|
||||
* same vertex.
|
||||
* same vertex.
|
||||
*
|
||||
* On the first iteration all vertices receive the `initialMsg` and
|
||||
* on subsequent iterations if a vertex does not receive a message then
|
||||
* the vertex-program is not invoked.
|
||||
* on subsequent iterations if a vertex does not receive a message
|
||||
* then the vertex-program is not invoked.
|
||||
*
|
||||
* This function iterates until there are no remaining messages.
|
||||
*
|
||||
|
|
@ -195,24 +197,26 @@ object Pregel {
|
|||
*
|
||||
* @param graph the input graph.
|
||||
*
|
||||
* @param initialMsg the message each vertex will receive at the
|
||||
* on the first iteration.
|
||||
* @param initialMsg the message each vertex will receive at the on
|
||||
* the first iteration.
|
||||
*
|
||||
* @param numIter the number of iterations to run this computation.
|
||||
*
|
||||
* @param vprog the user-defined vertex program which runs on each vertex
|
||||
* and receives the inbound message and computes a new vertex value.
|
||||
* On the first iteration the vertex program is invoked on all vertices
|
||||
* and is passed the default message. On subsequent iterations the
|
||||
* vertex program is only invoked on those vertices that receive messages.
|
||||
* @param vprog the user-defined vertex program which runs on each
|
||||
* vertex and receives the inbound message and computes a new vertex
|
||||
* value. On the first iteration the vertex program is invoked on
|
||||
* all vertices and is passed the default message. On subsequent
|
||||
* iterations the vertex program is only invoked on those vertices
|
||||
* that receive messages.
|
||||
*
|
||||
* @param sendMsg a user supplied function that is applied to out edges
|
||||
* of vertices that received messages in the current iteration.
|
||||
* @param sendMsg a user supplied function that is applied to out
|
||||
* edges of vertices that received messages in the current
|
||||
* iteration.
|
||||
*
|
||||
* @param mergeMsg a user supplied function that takes two incoming messages
|
||||
* of type A and merges them into a single message of type A.
|
||||
* ''This function must be commutative and associative and ideally the
|
||||
* size of A should not increase.''
|
||||
* @param mergeMsg a user supplied function that takes two incoming
|
||||
* messages of type A and merges them into a single message of type
|
||||
* A. ''This function must be commutative and associative and
|
||||
* ideally the size of A should not increase.''
|
||||
*
|
||||
* @return the resulting graph at the end of the computation
|
||||
*
|
||||
|
|
@ -228,18 +232,18 @@ object Pregel {
|
|||
|
||||
|
||||
/**
|
||||
* Execute a Pregel-like iterative vertex-parallel abstraction.
|
||||
* The user-defined vertex-program `vprog` is executed in parallel
|
||||
* on each vertex receiving any inbound messages and computing a new
|
||||
* Execute a Pregel-like iterative vertex-parallel abstraction. The
|
||||
* user-defined vertex-program `vprog` is executed in parallel on
|
||||
* each vertex receiving any inbound messages and computing a new
|
||||
* value for the vertex. The `sendMsg` function is then invoked on
|
||||
* all out-edges and is used to compute an optional message to the
|
||||
* destination vertex. The `mergeMsg` function is a commutative
|
||||
* all out-edges and is used to compute an optional message to the
|
||||
* destination vertex. The `mergeMsg` function is a commutative
|
||||
* associative function used to combine messages destined to the
|
||||
* same vertex.
|
||||
* same vertex.
|
||||
*
|
||||
* On the first iteration all vertices receive the `initialMsg` and
|
||||
* on subsequent iterations if a vertex does not receive a message then
|
||||
* the vertex-program is not invoked.
|
||||
* on subsequent iterations if a vertex does not receive a message
|
||||
* then the vertex-program is not invoked.
|
||||
*
|
||||
* This function iterates until there are no remaining messages.
|
||||
*
|
||||
|
|
@ -249,28 +253,29 @@ object Pregel {
|
|||
*
|
||||
* @param graph the input graph.
|
||||
*
|
||||
* @param initialMsg the message each vertex will receive at the
|
||||
* on the first iteration.
|
||||
* @param initialMsg the message each vertex will receive at the on
|
||||
* the first iteration.
|
||||
*
|
||||
* @param numIter the number of iterations to run this computation.
|
||||
*
|
||||
* @param sendDir the edge direction along which the `sendMsg` function
|
||||
* is invoked.
|
||||
* @param sendDir the edge direction along which the `sendMsg`
|
||||
* function is invoked.
|
||||
*
|
||||
* @param vprog the user-defined vertex program which runs on each vertex
|
||||
* and receives the inbound message and computes a new vertex value.
|
||||
* On the first iteration the vertex program is invoked on all vertices
|
||||
* and is passed the default message. On subsequent iterations the
|
||||
* vertex program is only invoked on those vertices that receive messages.
|
||||
* @param vprog the user-defined vertex program which runs on each
|
||||
* vertex and receives the inbound message and computes a new vertex
|
||||
* value. On the first iteration the vertex program is invoked on
|
||||
* all vertices and is passed the default message. On subsequent
|
||||
* iterations the vertex program is only invoked on those vertices
|
||||
* that receive messages.
|
||||
*
|
||||
* @param sendMsg a user supplied function that is applied to each edge
|
||||
* in the direction `sendDir` adjacent to vertices that received messages
|
||||
* in the current iteration.
|
||||
* @param sendMsg a user supplied function that is applied to each
|
||||
* edge in the direction `sendDir` adjacent to vertices that
|
||||
* received messages in the current iteration.
|
||||
*
|
||||
* @param mergeMsg a user supplied function that takes two incoming messages
|
||||
* of type A and merges them into a single message of type A.
|
||||
* ''This function must be commutative and associative and ideally the
|
||||
* size of A should not increase.''
|
||||
* @param mergeMsg a user supplied function that takes two incoming
|
||||
* messages of type A and merges them into a single message of type
|
||||
* A. ''This function must be commutative and associative and
|
||||
* ideally the size of A should not increase.''
|
||||
*
|
||||
* @return the resulting graph at the end of the computation
|
||||
*
|
||||
|
|
|
|||
Loading…
Reference in a new issue