Relational neighbor classifier

A relation model is based on the idea that the behavior between nodes is correlated, meaning that connected nodes have a propensity to belong to the same class. The relational neighbor classifier, in particular, predicts a node's class based on its neighboring nodes and adjacent edges.

The dataset transfers consists of transactions from different accounts. The account_info data contains which of these accounts are money mules. However, it is unknown whether account "I41" is a money mule. Predict the money mule propensity of account "I41" using a relational neighbor classifier.

This exercise is part of the course

Fraud Detection in R

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Exercise instructions

Create an undirected graph called net based on transfers. Set directed to the appropriate boolean (TRUE or FALSE).
Specify a color for each node: set V(net)$color to "darkorange" if account_info$isMoneyMule == TRUE and "slateblue1" otherwise.
Use subgraph() on net to create a subgraph called subset containing vertices "I41","I47", "I87" and "I20".
Use function strength() on subnet and on net to compute the money mule probability of node "I41" as the fraction of mule neighbors

Hands-on interactive exercise

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

# From data frame to graph
net <- graph_from_data_frame(___, directed = ___)

# Plot the network; color nodes according to isMoneyMule-variable
___(___)$color <- ifelse(___$___ == TRUE, ___, ___)
plot(net, vertex.label.color = "black", vertex.label.font = 2, vertex.size = 18)

# The id's of the money mule accounts:
print(account_info$id[account_info$isMoneyMule == TRUE])

# Create subgraph containing node "I41" and all money mules nodes "I47", "I87", "I20":
subnet <- ___(___, v = c(___))

# Compute the money mule probability of node "I41" based on the neighbors
___(___, v = "I41") / ___(___, v = "I41")

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