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.

Deze oefening maakt deel uit van de cursus

Predictive Analytics using Networked Data in R

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Oefeninstructies

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.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# 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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