Modularity measures strength of division of a network into communities (modules,clusters). Measures takes values from range <-1,1>. Value close to 1 indicates strong community structure. When Q=0 then the community division is not better than random.
Q=\sum_{i=1}^{k}{(e_{ii}-a_i^2)}
Where k is number of communities, e_{ii} is number of edges that has both ends in community i and a_i is number of edges with one end in community i
For further informations please refer to [lecture] and [Newman].
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 modularity: Double= graph.modularity()
// Modularity value for graphReferences:
| [lecture] | Carl Kingsford (2009). Modularity, PDF |
| [Newman] | Newman, M. E., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical review E, 69(2), 026113. PDF |