Dyadicity of churners
In this exercise, you will compute the dyadicity amongst the churners in the network to see if they share more or fewer edges than expected in a random configuration of the network.
The variables ChurnNodes, ChurnEdges, and connectance are available for you to use.
For expected dyadicity, use the formula \( \frac{n_ C\cdot (n_ C - 1)}{2} \cdot p\), where \(n_C\) is the number of churners, \(N\) is the number of nodes, and \(p\) is the connectance. Dyadicity of the churners is the ratio between the actual churners and the expected churn dyadicity.
This exercise is part of the course
Predictive Analytics using Networked Data in R
Exercise instructions
- Compute the expected dyadicity of churners and assign it to the variable
ExpectedDyadChurn. - Compute the dyadicity of the churners by dividing
ChurnEdgeswithExpectedDyadChurn. Call this valueDyadChurn. - Inspect
DyadChurn.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Compute the expected churn dyadicity
ExpectedDyadChurn <- ___ * (___) * connectance / 2
# Compute the churn dyadicity
DyadChurn <- ___ / ___
# Inspect the value
___