Degree of a node is number of connections that its has. When we have directed network, we can distinguish indegree (input edges) and outdegree (output edges). We can treate degree as a centrality measure. Nodes with high degree can be assumed as important. Ofcourse it depends on the sitution, and interpretations can differ.
For further informations please refer to [lecture].
Method returns a tuple (outdegree,indegree):(Int,Int). If computations will be done using treatAsUndirected, both values will be equal.
import ml.sparkling.graph.operators.OperatorsDSL._
import org.apache.spark.SparkContext
import org.apache.spark.graphx.Graph
implicit ctx:SparkContext=???
// initialize your SparkContext as implicit value
val graph =???
// load your graph (for example using Graph loading API)
val centralityGraph: Graph[(Int,Int), _] = graph.degreeCentrality()
// Graph where each vertex is associated with its degree centrality in form of tuple (outdegree,indegree):(Int,Int)You can also compute closeness centrality for graph treated it as undirected one:
import ml.sparkling.graph.operators.OperatorsDSL._
import org.apache.spark.SparkContext
import ml.sparkling.graph.api.operators.measures.VertexMeasureConfiguration
import org.apache.spark.graphx.Graph
implicit ctx:SparkContext=???
// initialize your SparkContext as implicit value
val graph =???
// load your graph (for example using Graph loading API)
val centralityGraph: Graph[Double, _] = graph.degreeCentrality(VertexMeasureConfiguration(treatAsUndirected=true))
// Graph where each vertex is associated with its degree centrality computed for undirected graph in form of tuple (degree,degree):(Int,Int)References:
| [lecture] | Dr. Cecilia Mascolo, Social and Technological Network'Analysis PDF |