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.

This exercise is part of the course

Predictive Analytics using Networked Data in R

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Exercise instructions

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.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Compute the number of churn neighbors
V(network)$ChurnNeighbors <- as.vector(___ %*% V(network)$___)

# Compute the number of non-churn neighbors
V(network)$___ <- as.vector(___ %*% (1 - V(network)$___))

# Compute the relational neighbor probability
V(network)$RelationalNeighbor <- as.vector(V(network)$___ / 
    (V(network)$___ + V(network)$___))

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