Exercise

# Link-based features

In this exercise, you will compute first order link-based features by multiplying the `Churn`

attribute of the network with the network's adjacency matrix.

Note, that since churn is a binary indicator, the attribute `Churn`

has 1 for churners and 0 for non-churners. Consequently, the attribute `1-Churn`

has 1 for non-churners and 0 for churners.
This is helpful when computing the number of non-churn neighbors.

Instructions

**100 XP**

- Compute the attribute
`ChurnNeighbors`

, i.e. the number of neighbors who churned, by multiplying`AdjacencyMatrix`

with the`Churn`

attribute of`network`

. Apply`as.vector()`

to the result and add it to the network. - Similarly, compute
`NonChurnNeighbors`

, i.e. the number of non-churn neighbors. - Calculate the attribute
`RelationalNeighbor`

, the ratio of churners in the neighborhood, by dividing`ChurnNeighbors`

with the sum of`ChurnNeighbors`

and`NonChurnNeighbors`

.