Adding scaladoc links

sparkling-graph · Mar 31, 2017 · b15f52f · b15f52f
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diff --git a/aapsp.rst b/aapsp.rst
@@ -23,7 +23,7 @@ Alghotim block scheme
 Examples
 ----------------------
 
-Alghoritm API lets to compute paths :
+Alghoritm `API`_ lets to compute paths :
 
 *	For single vertex:
 
@@ -72,3 +72,6 @@ Alghoritm API lets to compute paths :
 		val graphWithPaths =  ApproximatedShortestPathsAlgorithm.computeShortestPathsLengthsIterative(graph, (g:Graph[_,_])=>bucketSize)
 		val paths : VertexRDD[Iterable[(VertexId, JDouble)]  =  graphWithPaths.vertices
 
+
+
+.. _API: http://sparkling-graph.github.io/sparkling-graph/latest/api/#ml.sparkling.graph.operators.algorithms.aproximation.ApproximatedShortestPathsAlgorithm$
diff --git a/partitioning.rst b/partitioning.rst
@@ -3,6 +3,7 @@ Partitioning methods
 
 Library provides multiple methods for graph partitioning. By default GraphX provides only random methods, in SparklingGraph you can find approaches that are using structural properties of graphs in order to minimize computation times and storage overheads. 
 
+All methods can be found in `partitioning package`_
 
 
 Propagation bases
@@ -77,4 +78,9 @@ That is solution that use PSCAN alghoritm in conduction with epsilon parameter s
 	val graph = ???
 	// load your graph (for example using Graph loading API)
 	val numberOfRequiredPartitions=24
-	val partitionedGraph =  PSCANBasedPartitioning.partitionGraphBy(graph,numberOfRequiredPartitions)
+	val partitionedGraph =  PSCANBasedPartitioning.partitionGraphBy(graph,numberOfRequiredPartitions)
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+
+
+
+.. _partitioning package: http://sparkling-graph.github.io/sparkling-graph/latest/api/#ml.sparkling.graph.operators.partitioning.package